Interference management, handoff, power control and link adaptation in distributed-input distributed-output (DIDO) communication systems

ABSTRACT

A system and method are described herein employing a plurality of distributed transmitting antennas to create locations in space with zero RF energy. In one embodiment, when M transmit antennas are employed, it is possible to create up to (M−1) points of zero RF energy in predefined locations. In one embodiment of the invention, the points of zero RF energy are wireless devices and the transmit antennas are aware of the channel state information (CSI) between the transmitters and the receivers. In one embodiment, the CSI is computed at the receivers and fed back to the transmitters. In another embodiment, the CSI is computed at the transmitter via training from the receivers, assuming channel reciprocity is exploited. The transmitters may utilize the CSI to determine the interfering signals to be simultaneously transmitted. In one embodiment, block diagonalization (BD) precoding is employed at the transmit antennas to generate points of zero RF energy.

RELATED APPLICATIONS

This application is a continuation-in-part of the following co-pendingU.S. patent applications:

U.S. application Ser. No. 12/630,627, filed Dec. 3, 2009, entitled“System and Method For Distributed Antenna Wireless Communications”

U.S. application Ser. No. 12/143,503, filed Jun. 20, 2008 entitled“System and Method For Distributed Input-Distributed Output WirelessCommunications”;

U.S. application Ser. No. 11/894,394, filed Aug. 20, 2007 entitled,“System and Method for Distributed Input Distributed Output WirelessCommunications”;

U.S. application Ser. No. 11/894,362, filed Aug. 20, 2007 entitled,“System and method for Distributed Input-Distributed WirelessCommunications”;

U.S. application Ser. No. 11/894,540, filed Aug. 20, 2007 entitled“System and Method For Distributed Input-Distributed Output WirelessCommunications”

U.S. application Ser. No. 11/256,478, filed Oct. 21, 2005 entitled“System and Method For Spatial-Multiplexed Tropospheric ScatterCommunications”;

U.S. application Ser. No. 10/817,731, filed Apr. 2, 2004 entitled“System and Method For Enhancing Near Vertical Incidence Skywave(“NVIS”) Communication Using Space-Time Coding.

BACKGROUND

Prior art multi-user wireless systems may include only a single basestation or several base stations.

A single WiFi base station (e.g., utilizing 2.4 GHz 802.11b, g or nprotocols) attached to a broadband wired Internet connection in an areawhere there are no other WiFi access points (e.g. a WiFi access pointattached to DSL within a rural home) is an example of a relativelysimple multi-user wireless system that is a single base station that isshared by one or more users that are within its transmission range. If auser is in the same room as the wireless access point, the user willtypically experience a high-speed link with few transmission disruptions(e.g. there may be packet loss from 2.4 GHz interferers, like microwaveovens, but not from spectrum sharing with other WiFi devices), If a useris a medium distance away or with a few obstructions in the path betweenthe user and WiFi access point, the user will likely experience amedium-speed link. If a user is approaching the edge of the range of theWiFi access point, the user will likely experience a low-speed link, andmay be subject to periodic drop-outs if changes to the channel result inthe signal SNR dropping below usable levels. And, finally, if the useris beyond the range of the WiFi base station, the user will have no linkat all.

When multiple users access the WiFi base station simultaneously, thenthe available data throughput is shared among them. Different users willtypically place different throughput demands on a WiFi base station at agiven time, but at times when the aggregate throughput demands exceedthe available throughput from the WiFi base station to the users, thensome or all users will receive less data throughput than they areseeking. In an extreme situation where a WiFi access point is sharedamong a very large number of users, throughput to each user can slowdown to a crawl, and worse, data throughput to each user may arrive inshort bursts separated by long periods of no data throughput at all,during which time other users are served. This “choppy” data deliverymay impair certain applications, like media streaming.

Adding additional WiFi base stations in situations with a large numberof users will only help up to a point. Within the 2.4 GHz ISM band inthe U.S., there are 3 non-interfering channels that can be used forWiFi, and if 3 WiFi base stations in the same coverage area areconfigured to each use a different non-interfering channel, then theaggregate throughput of the coverage area among multiple users will beincreased up to a factor of 3. But, beyond that, adding more WiFi basestations in the same coverage area will not increase aggregatethroughput, since they will start sharing the same available spectrumamong them, effectually utilizing time-division multiplexed access(TDMA) by “taking turns” using the spectrum. This situation is oftenseen in coverage areas with high population density, such as withinmulti-dwelling units. For example, a user in a large apartment buildingwith a WiFi adapter may well experience very poor throughput due todozens of other interfering WiFi networks (e.g. in other apartments)serving other users that are in the same coverage area, even if theuser's access point is in the same room as the client device accessingthe base station. Although the link quality is likely good in thatsituation, the user would be receiving interference from neighbor WiFiadapters operating in the same frequency band, reducing the effectivethroughput to the user.

Current multiuser wireless systems, including both unlicensed spectrum,such as WiFi, and licensed spectrum, suffer from several limitations.These include coverage area, downlink (DL) data rate and uplink (UL)data rate. Key goals of next generation wireless systems, such as WiMAXand LTE, are to improve coverage area and DL and UL data rate viamultiple-input multiple-output (MIMO) technology. MIMO employs multipleantennas at transmit and receive sides of wireless links to improve linkquality (resulting in wider coverage) or data rate (by creating multiplenon-interfering spatial channels to every user). If enough data rate isavailable for every user (note, the terms “user” and “client” are usedherein interchangeably), however, it may be desirable to exploit channelspatial diversity to create non-interfering channels to multiple users(rather than single user), according to multiuser MIMO (MU-MIMO)techniques. See, e.g., the following references:

-   G. Caire and S. Shamai, “On the achievable throughput of a    multiantenna Gaussian broadcast channel,” IEEE Trans. Info. Th.,    vol. 49, pp. 1691-1706, July 2003.-   P. Viswanath and D. Tse, “Sum capacity of the vector Gaussian    broadcast channel and uplink-downlink duality,” IEEE Trans. Info.    Th., vol. 49, pp. 1912-1921, August 2003.-   S. Vishwanath, N. Jindal, and A. Goldsmith, “Duality, achievable    rates, and sum-rate capacity of Gaussian MIMO broadcast channels,”    IEEE Trans. Info. Th., vol. 49, pp. 2658-2668, October 2003.-   W. Yu and J. Cioffi, “Sum capacity of Gaussian vector broadcast    channels,” IEEE Trans. Info. Th., vol. 50, pp. 1875-1892, September    2004.-   M. Costa, “Writing on dirty paper,” IEEE Transactions on Information    Theory, vol. 29, pp. 439-441, May 1983.-   M. Bengtsson, “A pragmatic approach to multi-user spatial    multiplexing,” Proc. of Sensor Array and Multichannel Sign. Proc.    Workshop, pp. 130-134, August 2002.-   K.-K. Wong, R. D. Murch, and K. B. Letaief, “Performance enhancement    of multiuser MIMO wireless communication systems,” IEEE Trans.    Comm., vol. 50, pp. 1960-1970, December 2002.-   M. Sharif and B. Hassibi, “On the capacity of MIMO broadcast channel    with partial side information,” IEEE Trans. Info. Th., vol. 51, pp.    506-522, February 2005.

For example, in MIMO 4×4 systems (i.e., four transmit and four receiveantennas), 10 MHz bandwidth, 16-QAM modulation and forward errorcorrection (FEC) coding with rate ¾ (yielding spectral efficiency of 3bps/Hz), the ideal peak data rate achievable at the physical layer forevery user is 4×30 Mbps=120 Mbps, which is much higher than required todeliver high definition video content (which may only require ˜10 Mbps).In MU-MIMO systems with four transmit antennas, four users and singleantenna per user, in ideal scenarios (i.e., independent identicallydistributed, i.i.d., channels) downlink data rate may be shared acrossthe four users and channel spatial diversity may be exploited to createfour parallel 30 Mbps data links to the users. Different MU-MIMO schemeshave been proposed as part of the LTE standard as described, forexample, in 3GPP, “Multiple Input Multiple Output in UTRA”, 3GPP TR25.876 V7.0.0, March 2007; 3GPP, “Base Physical channels andmodulation”, TS 36.211, V8.7.0, May 2009; and 3GPP, “Multiplexing andchannel coding”, TS 36.212, V8.7.0, May 2009. However, these schemes canprovide only up to 2× improvement in DL data rate with four transmitantennas. Practical implementations of MU-MIMO techniques in standardand proprietary cellular systems by companies like ArrayComm (see, e.g.,ArrayComm, “Field-proven results”,http://www.arraycomm.com/serve.php?page=proof) have yielded up to a ˜3×increase (with four transmit antennas) in DL data rate via spacedivision multiple access (SDMA). A key limitation of MU-MIMO schemes incellular networks is lack of spatial diversity at the transmit side.Spatial diversity is a function of antenna spacing and multipath angularspread in the wireless links. In cellular systems employing MU-MIMOtechniques, transmit antennas at a base station are typically clusteredtogether and placed only one or two wavelengths apart due to limitedreal estate on antenna support structures (referred to herein as“towers,” whether physically tall or not) and due to limitations onwhere towers may be located. Moreover, multipath angular spread is lowsince cell towers are typically placed high up (10 meters or more) aboveobstacles to yield wider coverage.

Other practical issues with cellular system deployment include excessivecost and limited availability of locations for cellular antennalocations (e.g. due to municipal restrictions on antenna placement, costof real-estate, physical obstructions, etc.) and the cost and/oravailability of network connectivity to the transmitters (referred toherein as “backhaul”). Further, cellular systems often have difficultyreaching clients located deeply in buildings due to losses from walls,ceilings, floors, furniture and other impediments.

Indeed, the entire concept of a cellular structure for wide-area networkwireless presupposes a rather rigid placement of cellular towers, analternation of frequencies between adjacent cells, and frequentlysectorization, so as to avoid interference among transmitters (eitherbase stations or users) that are using the same frequency. As a result,a given sector of a given cell ends up being a shared block of DL and ULspectrum among all of the users in the cell sector, which is then sharedamong these users primarily in only the time domain. For example,cellular systems based on Time Division Multiple Access (TDMA) and CodeDivision Multiple Access (CDMA) both share spectrum among users in thetime domain. By overlaying such cellular systems with sectorization,perhaps a 2-3× spatial domain benefit can be achieved. And, then byoverlaying such cellular systems with a MU-MIMO system, such as thosedescribed previously, perhaps another 2-3× space-time domain benefit canbe achieved. But, given that the cells and sectors of the cellularsystem are typically in fixed locations, often dictated by where towerscan be placed, even such limited benefits are difficult to exploit ifuser density (or data rate demands) at a given time does not match upwell with tower/sector placement. A cellular smart phone user oftenexperiences the consequence of this today where the user may be talkingon the phone or downloading a web page without any trouble at all, andthen after driving (or even walking) to a new location will suddenly seethe voice quality drop or the web page slow to a crawl, or even lose theconnection entirely. But, on a different day, the user may have theexact opposite occur in each location. What the user is probablyexperiencing, assuming the environmental conditions are the same, is thefact that user density (or data rate demands) is highly variable, butthe available total spectrum (and thereby total data rate, using priorart techniques) to be shared among users at a given location is largelyfixed.

Further, prior art cellular systems rely upon using differentfrequencies in different adjacent cells, typically 3 differentfrequencies. For a given amount of spectrum, this reduces the availabledata rate by 3×.

So, in summary, prior art cellular systems may lose perhaps 3× inspectrum utilization due to cellularization, and may improve spectrumutilization by perhaps 3× through sectorization and perhaps 3× morethrough MU-MIMO techniques, resulting in a net 3*3/3=3× potentialspectrum utilization. Then, that bandwidth is typically divided up amongusers in the time domain, based upon what sector of what cell the usersfall into at a given time. There are even further inefficiencies thatresult due to the fact that a given user's data rate demands aretypically independent of the user's location, but the available datarate varies depending on the link quality between the user and the basestation. For example, a user further from a cellular base station willtypically have less available data rate than a user closer to a basestation. Since the data rate is typically shared among all of the usersin a given cellular sector, the result of this is that all users areimpacted by high data rate demands from distant users with poor linkquality (e.g. on the edge of a cell) since such users will still demandthe same amount of data rate, yet they will be consuming more of theshared spectrum to get it.

Other proposed spectrum sharing systems, such as that used by WiFi(e.g., 802.11b, g, and n) and those proposed by the White SpacesCoalition, share spectrum very inefficiently since simultaneoustransmissions by base stations within range of a user result ininterference, and as such, the systems utilize collision avoidance andsharing protocols. These spectrum sharing protocols are within the timedomain, and so, when there are a large number of interfering basestations and users, no matter how efficient each base station itself isin spectrum utilization, collectively the base stations are limited totime domain sharing of the spectrum among each other. Other prior artspectrum sharing systems similarly rely upon similar methods to mitigateinterference among base stations (be they cellular base stations withantennas on towers or small scale base stations, such as WiFi AccessPoints (APs)). These methods include limiting transmission power fromthe base station so as to limit the range of interference, beamforming(via synthetic or physical means) to narrow the area of interference,time-domain multiplexing of spectrum and/or MU-MIMO techniques withmultiple clustered antennas on the user device, the base station orboth. And, in the case of advanced cellular networks in place or plannedtoday, frequently many of these techniques are used at once.

But, what is apparent by the fact that even advanced cellular systemscan achieve only about a 3× increase in spectrum utilization compared toa single user utilizing the spectrum is that all of these techniqueshave done little to increase the aggregate data rate among shared usersfor a given area of coverage. In particular, as a given coverage areascales in terms of users, it becomes increasingly difficult to scale theavailable data rate within a given amount of spectrum to keep pace withthe growth of users. For example, with cellular systems, to increase theaggregate data rate within a given area, typically the cells aresubdivided into smaller cells (often called nano-cells or femto-cells).Such small cells can become extremely expensive given the limitations onwhere towers can be placed, and the requirement that towers must beplaced in a fairly structured pattern so as to provide coverage with aminimum of “dead zones”, yet avoid interference between nearby cellsusing the same frequencies. Essentially, the coverage area must bemapped out, the available locations for placing towers or base stationsmust be identified, and then given these constraints, the designers ofthe cellular system must make do with the best they can. And, of course,if user data rate demands grow over time, then the designers of thecellular system must yet again remap the coverage area, try to findlocations for towers or base stations, and once again work within theconstraints of the circumstances. And, very often, there simply is nogood solution, resulting in dead zones or inadequate aggregate data ratecapacity in a coverage area. In other words, the rigid physicalplacement requirements of a cellular system to avoid interference amongtowers or base stations utilizing the same frequency results insignificant difficulties and constraints in cellular system design, andoften is unable to meet user data rate and coverage requirements.

So-called prior art “cooperative” and “cognitive” radio systems seek toincrease the spectral utilization in a given area by using intelligentalgorithms within radios such that they can minimize interference amongeach other and/or such that they can potentially “listen” for otherspectrum use so as to wait until the channel is clear. Such systems areproposed for use particularly in unlicensed spectrum in an effort toincrease the spectrum utilization of such spectrum.

A mobile ad hoc network (MANET) (seehttp://en.wikipedia.org/wiki/Mobile_ad_hoc_network) is an example of acooperative self-configuring network intended to provide peer-to-peercommunications, and could be used to establish communication amongradios without cellular infrastructure, and with sufficiently low-powercommunications, can potentially mitigate interference among simultaneoustransmissions that are out of range of each other. A vast number ofrouting protocols have been proposed and implemented for MANET systems(see http://en.wikipedia.org/wiki/List_of_ad-hoc_routing_protocols for alist of dozens of routing protocols in a wide range of classes), but acommon theme among them is they are all techniques for routing (e.g.repeating) transmissions in such a way to minimize transmitterinterference within the available spectrum, towards the goal ofparticular efficiency or reliability paradigms.

All of the prior art multi-user wireless systems seek to improvespectrum utilization within a given coverage area by utilizingtechniques to allow for simultaneous spectrum utilization among basestations and multiple users. Notably, in all of these cases, thetechniques utilized for simultaneous spectrum utilization among basestations and multiple users achieve the simultaneous spectrum use bymultiple users by mitigating interference among the waveforms to themultiple users. For example, in the case of 3 base stations each using adifferent frequency to transmit to one of 3 users, there interference ismitigated because the 3 transmissions are at 3 different frequencies. Inthe case of sectorization from a base station to 3 different users, each180 degrees apart relative to the base station, interference ismitigated because the beamforming prevents the 3 transmissions fromoverlapping at any user.

When such techniques are augmented with MU-MIMO, and, for example, eachbase station has 4 antennas, then this has the potential to increasedownlink throughput by a factor of 4, by creating four non-interferingspatial channels to the users in given coverage area. But it is stillthe case that some technique must be utilized to mitigate theinterference among multiple simultaneous transmissions to multiple usersin different coverage areas.

And, as previously discussed, such prior art techniques (e.g.cellularization, sectorization) not only typically suffer fromincreasing the cost of the multi-user wireless system and/or theflexibility of deployment, but they typically run into physical orpractical limitations of aggregate throughput in a given coverage area.For example, in a cellular system, there may not be enough availablelocations to install more base stations to create smaller cells. And, inan MU-MIMO system, given the clustered antenna spacing at each basestation location, the limited spatial diversity results inasymptotically diminishing returns in throughput as more antennas areadded to the base station.

And further, in the case of multi-user wireless systems where the userlocation and density is unpredictable, it results in unpredictable (withfrequently abrupt changes) in throughput, which is inconvenient to theuser and renders some applications (e.g. the delivery of servicesrequiring predictable throughput) impractical or of low quality. Thus,prior art multi-user wireless systems still leave much to be desired interms of their ability to provide predictable and/or high-qualityservices to users.

Despite the extraordinary sophistication and complexity that has beendeveloped for prior art multi-user wireless systems over time, thereexist common themes: transmissions are distributed among different basestations (or ad hoc transceivers) and are structured and/or controlledso as to avoid the RF waveform transmissions from the different basestations and/or different ad hoc transceivers from interfering with eachother at the receiver of a given user.

Or, to put it another way, it is taken as a given that if a user happensto receive transmissions from more than one base station or ad hoctransceiver at the same time, the interference from the multiplesimultaneous transmissions will result in a reduction of the SNR and/orbandwidth of the signal to the user which, if severe enough, will resultin loss of all or some of the potential data (or analog information)that would otherwise have been received by the user.

Thus, in a multiuser wireless system, it is necessary to utilize one ormore spectrum sharing approaches or another to avoid or mitigate suchinterference to users from multiple base stations or ad hoc transceiverstransmitting at the same frequency at the same time. There are a vastnumber of prior art approaches to avoiding such interference, includingcontrolling base stations' physical locations (e.g. cellularization),limiting power output of base stations and/or ad hoc transceivers (e.g.limiting transmit range), beamforming/sectorization, and time domainmultiplexing. In short, all of these spectrum sharing systems seek toaddress the limitation of multiuser wireless systems that when multiplebase stations and/or ad hoc transceivers transmitting simultaneously atthe same frequency are received by the same user, the resultinginterference reduces or destroys the data throughput to the affecteduser. If a large percentage, or all, of the users in the multi-userwireless system are subject to interference from multiple base stationsand/or ad hoc transceivers (e.g. in the event of the malfunction of acomponent of a multi-user wireless system), then it can result in asituation where the aggregate throughput of the multi-user wirelesssystem is dramatically reduced, or even rendered non-functional.

Prior art multi-user wireless systems add complexity and introducelimitations to wireless networks and frequently result in a situationwhere a given user's experience (e.g. available bandwidth, latency,predictability, reliability) is impacted by the utilization of thespectrum by other users in the area. Given the increasing demands foraggregate bandwidth within wireless spectrum shared by multiple users,and the increasing growth of applications that can rely upon multi-userwireless network reliability, predictability and low latency for a givenuser, it is apparent that prior art multi-user wireless technologysuffers from many limitations. Indeed, with the limited availability ofspectrum suitable for particular types of wireless communications (e.g.at wavelengths that are efficient in penetrating building walls), it maybe the case that prior art wireless techniques will be insufficient tomeet the increasing demands for bandwidth that is reliable, predictableand low-latency.

Prior art related to the current invention describes beamforming systemsand methods for null-steering in multiuser scenarios. Beamforming wasoriginally conceived to maximize received signal-to-noise ratio (SNR) bydynamically adjusting phase and/or amplitude of the signals (i.e.,beamforming weights) fed to the antennas of the array, thereby focusingenergy toward the user's direction [14-20]. In multiuser scanarios,beamforming can be used to suppress interfering sources and maximizesignal-to-interference-plus-noise ratio (SINR) [21-23]. For example,when beamforming is used at the receiver of a wireless link, the weightsare computed to create nulls in the direction of the interfering sources[15]. When beamforming is used at the transmitter in multiuser downlinkscenarios, the weights are calculated to pre-cancel inter-userinterfence and maximize the SINR to every user [21-23]. Alternativetechniques for multiuser systems, such as BD precoding [24-25], computethe precoding weights to maximize throughput in the downlink broadcastchannel.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent publication with color drawing(s)will be provided by the U.S. Patent and Trademark Office upon requestand payment of the necessary fee.

A better understanding of the present invention can be obtained from thefollowing detailed description in conjunction with the drawings, inwhich:

FIG. 1 illustrates a prior art MIMO system.

FIG. 2 illustrates an N-antenna Base Station communicating with aplurality of Single-antenna Client Devices.

FIG. 3 illustrates a three Antenna Base Station communicating with threeSingle-Antenna Client Devices

FIG. 4 illustrates training signal techniques employed in one embodimentof the invention.

FIG. 5 illustrates channel characterization data transmitted from aclient device to a base station according to one embodiment of theinvention.

FIG. 6 illustrates a Multiple-Input Distributed-Output (“MIDO”)downstream transmission according to one embodiment of the invention.

FIG. 7 illustrates a Multiple-Input Multiple Output (“MIMO”) upstreamtransmission according to one embodiment of the invention.

FIG. 8 illustrates a base station cycling through different clientgroups to allocate throughput according to one embodiment of theinvention.

FIG. 9 illustrates a grouping of clients based on proximity according toone embodiment of the invention.

FIG. 10 illustrates an embodiment of the invention employed within anNVIS system.

FIG. 11 illustrates an embodiment of the DIDO transmitter with I/Qcompensation functional units.

FIG. 12 a DIDO receiver with I/Q compensation functional units.

FIG. 13 illustrates one embodiment of DIDO-OFDM systems with I/Qcompensation.

FIG. 14 illustrates one embodiment of DIDO 2×2 performance with andwithout I/Q compensation.

FIG. 15 illustrates one embodiment of DIDO 2×2 performance with andwithout I/Q compensation.

FIG. 16 illustrates one embodiment of the SER (Symbol Error Rate) withand without I/Q compensation for different QAM constellations.

FIG. 17 illustrates one embodiment of DIDO 2×2 performances with andwithout compensation in different user device locations.

FIG. 18 illustrates one embodiment of the SER with and without I/Qcompensation in ideal (i.i.d. (independent and identically-distributed))channels.

FIG. 19 illustrates one embodiment of a transmitter framework ofadaptive DIDO systems.

FIG. 20 illustrates one embodiment of a receiver framework of adaptiveDIDO systems.

FIG. 21 illustrates one embodiment of a method of adaptive DIDO-OFDM.

FIG. 22 illustrates one embodiment of the antenna layout for DIDOmeasurements.

FIG. 23 illustrates embodiments of array configurations for differentorder DIDO systems.

FIG. 24 illustrates the performance of different order DIDO systems.

FIG. 25 illustrates one embodiment of the antenna layout for DIDOmeasurements.

FIG. 26 illustrates one embodiment of the DIDO 2×2 performance with4-QAM and FEC rate ½ as function of the user device location.

FIG. 27 illustrates one embodiment of the antenna layout for DIDOmeasurements.

FIG. 28 illustrates how, in one embodiment, DIDO 8×8 yields larger SEthan DIDO 2×2 for lower TX power requirement.

FIG. 29 illustrates one embodiment of DIDO 2×2 performance with antennaselection.

FIG. 30 illustrates average bit error rate (BER) performance ofdifferent DIDO precoding schemes in i.i.d. channels.

FIG. 31 illustrates the signal to noise ratio (SNR) gain of ASel as afunction of the number of extra transmit antennas in i.i.d. channels.

FIG. 32 illustrates the SNR thresholds as a function of the number ofusers (M) for block diagnalization (BD) and ASel with 1 and 2 extraantennas in i.i.d. channels.

FIG. 33 illustrates the BER versus per-user average SNR for two userslocated at the same angular direction with different values of AngleSpread (AS).

FIG. 34 illustrates similar results as FIG. 33, but with higher angularseparation between the users.

FIG. 35 plots the SNR thresholds as a function of the AS for differentvalues of the mean angles of arrival (AOAs) of the users.

FIG. 36 illustrates the SNR threshold for an exemplary case of fiveusers.

FIG. 37 provides a comparison of the SNR threshold of BD and ASel, with1 and 2 extra antennas, for two user case.

FIG. 38 illustrates similar results as FIG. 37, but for a five usercase.

FIG. 39 illustrates the SNR thresholds for a BD scheme with differentvalues of AS.

FIG. 40 illustrates the SNR thresholds in spatially correlated channelswith AS=0.1° for BD and ASel with 1 and 2 extra antennas.

FIG. 41 illustrates the computation of the SNR thresholds for two morechannel scenarios with AS=5°.

FIG. 42 illustrates the computation of the SNR thresholds for two morechannel scenarios with AS=10°.

FIGS. 43-44 illustrate the SNR thresholds as a function of the number ofusers (M) and angle spread (AS) for BD and ASel schemes, with 1 and 2extra antennas, respectively.

FIG. 45 illustrates a receiver equipped with frequency offsetestimator/compensator.

FIG. 46 illustrates DIDO 2×2 system model according to one embodiment ofthe invention.

FIG. 47 illustrates a method according to one embodiment of theinvention.

FIG. 48 illustrates SER results of DIDO 2×2 systems with and withoutfrequency offset.

FIG. 49 compares the performance of different DIDO schemes in terms ofSNR thresholds.

FIG. 50 compares the amount of overhead required for differentembodiments of methods.

FIG. 51 illustrates a simulation with a small frequency offset off_(max)=2 Hz and no integer offset correction.

FIG. 52 illustrates results when turning off the integer offsetestimator.

FIG. 53 illustrates downlink spectral efficiency (SE) in [bps/Hz] as afunction of mutual information in [bps/Hz].

FIG. 54 illustrates average per-user symbol error rare (SER) performanceas a function of the mutual information in [bps/Hz].

FIG. 55 illustrates average per-user SER performance as a function ofthe minimum mutual information in [bps/Hz] and the thresholds used toswitch between different DIDO modes.

FIG. 56 illustrates average per-user SER vs. SNR for fixed modulationand adaptive DIDO systems.

FIG. 57 illustrates downlink SE vs. SNR for fixed modulation andadaptive DIDO systems.

FIG. 58 illustrates average per-user SER vs. SNR for adaptive DIDOsystems with different thresholds.

FIG. 59 illustrates downlink SE vs. SNR for adaptive DIDO systems withdifferent thresholds

FIG. 60 illustrates average per-user SER performance as a function ofthe minimum singular value of the effective channel matrix and the CQIthreshold for 4-QAM constellation.

FIG. 61 illustrates one embodiment of a circular topology of basetransceiver stations (DIDO antennas)

FIG. 62 illustrates an one embodiment of an alternate arrangement ofDIDO antennas.

FIG. 63 illustrates one embodiment in which a base station network (BSN)is used to deliver precoded baseband data from the centralizedprocessors (CPs) to DIDO antennas.

FIG. 64 illustrates one embodiment in which the BSN is used to carrymodulated signals.

FIG. 65 illustrates one embodiment comprised of two DIDO base stationsperfectly synchronized and two users with Line Of Sight (LOS) channels

FIG. 66 illustrates the path loss of DIDO at 85 MHz and 400 MHz usingthe Hata-Okumura model.

FIG. 67 illustrates the period maximum delay between channel stateinformation and data transmission as a function of the relative velocitybetween transmitter and receiver for different frequencies in the UHFband.

FIG. 68 illustrates propagation effects in DIDO systems for threedifferent carrier frequencies.

FIG. 69 illustrates the areas in the US territory currently covered bytransceiver stations operating in the Maritime band. The colors identifythe number of active channels (out of the 146 channels available in theMaritime band) that would cause harmful interference to DIDO-NVISstations at any location.

FIG. 70 illustrates sunspot number from the January 1900 throughout June2009.

FIG. 71 illustrates the path loss of WiMAX, LTE and NVIS systems.

FIG. 72 illustrates the locations of DIDO-NVIS transmitter (TX) andreceiver (RX) stations

FIG. 73 illustrates DIDO-NVIS receive antenna location. “lambda” denotesthe wavelength at 3.9 MHz (˜77 meters)

FIG. 74 illustrates typical 4-QAM constellations demodulated at threeusers' locations over DIDO-NVIS links.

FIG. 75 illustrates SER as a function of PU-SNR for DIDO-NVIS 3×3.

FIG. 76 illustrates DIDO-NVIS cells across the territory of the 48contiguous states of the USA.

FIG. 77 illustrates a main DIDO cluster surrounded by neighboring DIDOclusters in one embodiment of the invention.

FIG. 78 illustrates frequency division multiple access (FDMA) techniquesemployed in one embodiment of the invention.

FIG. 79 illustrates time division multiple access (TDMA) techniquesemployed in one embodiment of the invention.

FIG. 80 illustrates different types of interfering zones addressed inone embodiment of the invention.

FIG. 81 illustrates a framework employed in one embodiment of theinvention.

FIG. 82 illustrates a graph showing SER as a function of the SNR,assuming SIR=10 dB for the target client in the interfering zone.

FIG. 83 illustrates a graph showing SER derived from two IDCI-precodingtechniques.

FIG. 84 illustrates an exemplary scenario in which a target client movesfrom a main DIDO cluster to an interfering cluster.

FIG. 85 illustrates the signal-to-interference-plus-noise ratio (SINR)as a function of distance (D).

FIG. 86 illustrates the symbol error rate (SER) performance of the threescenarios for 4-QAM modulation in flat-fading narrowband channels.

FIG. 87 illustrates a method for IDCI precoding according to oneembodiment of the invention.

FIG. 88 illustrates the SINR variation in one embodiment as a functionof the client's distance from the center of main DIDO clusters.

FIG. 89 illustrates one embodiment in which the SER is derived for 4-QAMmodulation.

FIG. 90 illustrates one embodiment of the invention in which a finitestate machine implements a handoff algorithm.

FIG. 91 illustrates depicts one embodiment of a handoff strategy in thepresence of shadowing.

FIG. 92 illustrates a the hysteresis loop mechanism when switchingbetween any two states in FIG. 93.

FIG. 93 illustrates one embodiment of a DIDO system with power control.

FIG. 94 illustrates the SER versus SNR assuming four DIDO transmitantennas and four clients in different scenarios.

FIG. 95 illustrates MPE power density as a function of distance from thesource of RF radiation for different values of transmit power accordingto one embodiment of the invention.

FIGS. 96A-B illustrate different distributions of low-power andhigh-power DIDO distributed antennas.

FIGS. 97A-B illustrate two power distributions corresponding to theconfigurations in FIGS. 96A and 96B, respectively.

FIGS. 98A-B illustrate the rate distribution for the two scenarios shownin FIGS. 96A and 96B, respectively.

FIG. 99 illustrates one embodiment of a DIDO system with power control.

FIG. 100 illustrates one embodiment of a method which iterates acrossall antenna groups according to Round-Robin scheduling policy fortransmitting data.

FIG. 101 illustrates a comparison of the uncoded SER performance ofpower control with antenna grouping against conventional eigenmodeselection in U.S. Pat. No. 7,636,381.

FIGS. 102A-C illustrate thee scenarios in which BD precoding dynamicallyadjusts the precoding weights to account for different power levels overthe wireless links between DIDO antennas and clients.

FIG. 103 illustrates the amplitude of low frequency selective channels(assuming β=1) over delay domain or instantaneous PDP (upper plot) andfrequency domain (lower plot) for DIDO 2×2 systems

FIG. 104 illustrates one embodiment of a channel matrix frequencyresponse for DIDO 2×2, with a single antenna per client.

FIG. 105 illustrates one embodiment of a channel matrix frequencyresponse for DIDO 2×2, with a single antenna per client for channelscharacterized by high frequency selectivity (e.g., with β=0.1).

FIG. 106 illustrates exemplary SER for different QAM schemes (i.e.,4-QAM, 16-QAM, 64-QAM).

FIG. 107 illustrates one embodiment of a method for implementing linkadaptation (LA) techniques.

FIG. 108 illustrates SER performance of one embodiment of the linkadaptation (LA) techniques.

FIG. 109 illustrates the entries of the matrix in equation (28) as afunction of the OFDM tone index for DIDO 2×2 systems with N_(FFT)=64 andL₀=8.

FIG. 110 illustrates the SER versus SNR for L₀=8, M=N_(t)=2 transmitantennas and a variable number of P.

FIG. 111 illustrates the SER performance of one embodiment of aninterpolation method for different DIDO orders and L₀=16.

DETAILED DESCRIPTION

One solution to overcome many of the above prior art limitations is anembodiment of Distributed-Input Distributed-Output (DIDO) technology.DIDO technology is described in the following patents and patentapplications, all of which are assigned the assignee of the presentpatent and are incorporated by reference. These patents and applicationsare sometimes referred to collectively herein as the “related patentsand applications”:

U.S. application Ser. No. 12/630,627, filed Dec. 2, 2009, entitled“System and Method For Distributed Antenna Wireless Communications”

U.S. Pat. No. 7,599,420, filed Aug. 20, 2007, issued Oct. 6, 2009,entitled “System and Method for Distributed Input Distributed OutputWireless Communication”;

U.S. Pat. No. 7,633,994, filed Aug. 20, 2007, issued Dec. 15, 2009,entitled “System and Method for Distributed Input Distributed OutputWireless Communication”;

U.S. Pat. No. 7,636,381, filed Aug. 20, 2007, issued Dec. 22, 2009,entitled “System and Method for Distributed Input Distributed OutputWireless Communication”;

U.S. application Ser. No. 12/143,503, filed Jun. 20, 2008 entitled,“System and Method For Distributed Input-Distributed Output WirelessCommunications”;

U.S. application Ser. No. 11/256,478, filed Oct. 21, 2005 entitled“System and Method For Spatial-Multiplexed Tropospheric ScatterCommunications”;

U.S. Pat. No. 7,418,053, filed Jul. 30, 2004, issued Aug. 26, 2008,entitled “System and Method for Distributed Input Distributed OutputWireless Communication”;

U.S. application Ser. No. 10/817,731, filed Apr. 2, 2004 entitled“System and Method For Enhancing Near Vertical Incidence Skywave(“NVIS”) Communication Using Space-Time Coding.

For organization purposes, the present detailed description is separatedinto the following sections:

I. Disclosure of the Present Application which includes new matterintroduced in the present application and includes FIGS. 80-113 andassociated text;

II. Disclosure From Certain Related Applications which includes matterpreviously disclosed in certain related applications and which includesFIGS. 1-60 and associated text; and

III. Disclosure From U.S. application Ser. No. 12/630,627 which includesnew matter introduced in the most recent related application in thisseries (having Ser. No. 12/630,627) and which includes FIGS. 61-79 andassociated text.

Note that section I (Disclosure of the Present Application) utilizes itsown set of endnotes which refer to prior art references and priorapplications assigned to the assignee of the present application. Theendnote citations are listed at the end of section I (just prior to theheading for Section II). Citations in Sections II and III may havenumerical designations which overlap with those used in Section I eventhrough these numerical designations identify different references(which are identified within each respective section). Thus, referencesidentified by a particular numerical designation may be identifiedwithin the section in which the numerical designation is used.

I. Disclosure of the Present Application

1. Methods to Remove Inter-Cluster Interference

Described below are wireless radio frequency (RF) communication systemsand methods employing a plurality of distributed transmitting antennasto create locations in space with zero RF energy. When M transmitantennas are employed, it is possible to create up to (M−1) points ofzero RF energy in predefined locations. In one embodiment of theinvention, the points of zero RF energy are wireless devices and thetransmit antennas are aware of the channel state information (CSI)between the transmitters and the receivers. In one embodiment, the CSIis computed at the receivers and fed back to the transmitters. Inanother embodiment, the CSI is computed at the transmitter via trainingfrom the receivers, assuming channel reciprocity is exploited. Thetransmitters may utilize the CSI to determine the interfering signals tobe simultaneously transmitted. In one embodiment, block diagonalization(BD) precoding is employed at the transmit antennas to generate pointsof zero RF energy.

The system and methods described herein differ from the conventionalreceive/transmit beamforming techniques described above. In fact,receive beamforming computes the weights to suppress interference at thereceive side (via null-steering), whereas some embodiments of theinvention described herein apply weights at the transmit side to createinterference patters that result in one or multiple locations in spacewith “zero RF energy.” Unlike conventional transmit beamforming or BDprecoding designed to maximize signal quality (or SINR) to every user ordownlink throughput, respectively, the systems and methods describedherein minimize signal quality under certain conditions and/or fromcertain transmitters, thereby creating points of zero RF energy at theclient devices (sometimes referred to herein as “users”). Moreover, inthe context of distributed-input distributed-output (DIDO) systems(described in our related patents and applications), transmit antennasdistributed in space provide higher degrees of freedom (i.e., higherchannel spatial diversity) that can be exploited to create multiplepoints of zero RF energy and/or maximum SINR to different users. Forexample, with M transmit antennas it is possible to create up to (M−1)points of RF energy. By contrast, practical beamforming or BD multiusersystems are typically designed with closely spaced antennas at thetransmit side that limit the number of simultaneous users that can beserviced over the wireless link, for any number of transmit antennas M.

Consider a system with M transmit antennas and K users, with K<M. Weassume the transmitter is aware of the CSI (H∈

^(K×M)) between the M transmit antennas and K users. For simplicity,every user is assumed to be equipped with single antenna, but the samemethod can be extended to multiple receive antennas per user. Theprecoding weights (w∈

^(M×1)) that create zero RF energy at the K users' locations arecomputed to satisfy the following conditionHw=0^(K×1)where 0^(K×1) is the vector with all zero entries and H is the channelmatrix obtained by combining the channel vectors (h_(k) ∈

^(1×M)) from the M transmit antennas to the K users as

$H = {\begin{bmatrix}h_{1} \\\vdots \\h_{k} \\\vdots \\h_{K}\end{bmatrix}.}$In one embodiment, singular value decomposition (SVD) of the channelmatrix H is computed and the precoding weight w is defined as the rightsingular vector corresponding to the null subspace (identified by zerosingular value) of H. The transmit antennas employ the weight vectordefined above to transmit RF energy, while creating K points of zero RFenergy at the locations of the K users such that the signal received atthe k^(th) user is given byr _(k) =h _(k) ws _(k) +n _(k)=0+n _(k)where n_(k) ∈

^(1×1) is the additive white Gaussian noise (AWGN) at the k^(th) user.In one embodiment, singular value decomposition (SVD) of the channelmatrix H is computed and the precoding weight w is defined as the rightsingular vector corresponding to the null subspace (identified by zerosingular value) of H.

In another embodiment, the wireless system is a DIDO system and pointsof zero RF energy are created to pre-cancel interference to the clientsbetween different DIDO coverage areas. In U.S. application Ser. No.12/630,627, a DIDO system is described which includes:

-   -   DIDO clients    -   DIDO distributed antennas    -   DIDO base transceiver stations (BTS)    -   DIDO base station network (BSN)        Every BTS is connected via the BSN to multiple distributed        antennas that provide service to given coverage area called DIDO        cluster. In the present patent application we describe a system        and method for removing interference between adjacent DIDO        clusters. As illustrated in FIG. 77, we assume the main DIDO        cluster hosts the client (i.e. a user device served by the        multi-user DIDO system) affected by interference (or target        client) from the neighbor clusters.

In one embodiment, neighboring clusters operate at different frequenciesaccording to frequency division multiple access (FDMA) techniquessimilar to conventional cellular systems. For example, with frequencyreuse factor of 3, the same carrier frequency is reused every third DIDOcluster as illustrated in FIG. 78. In FIG. 78, the different carrierfrequencies are identified as F₁, F₂ and F₃. While this embodiment maybe used in some implementations, this solution yields loss in spectralefficiency since the available spectrum is divided in multiple subbandsand only a subset of DIDO clusters operate in the same subband.Moreover, it requires complex cell planning to associate different DIDOclusters to different frequencies, thereby preventing interference. Likeprior art cellular systems, such cellular planning requires specificplacement of antennas and limiting of transmit power to as to avoidinterference between clusters using the same frequency.

In another embodiment, neighbor clusters operate in the same frequencyband, but at different time slots according to time division multipleaccess (TDMA) technique. For example, as illustrated in FIG. 79 DIDOtransmission is allowed only in time slots T₁, T₂, and T₃ for certainclusters, as illustrated. Time slots can be assigned equally todifferent clusters, such that different clusters are scheduled accordingto a Round-Robin policy. If different clusters are characterized bydifferent data rate requirements (i.e., clusters in crowded urbanenvironments as opposed to clusters in rural areas with fewer number ofclients per area of coverage), different priorities are assigned todifferent clusters such that more time slots are assigned to theclusters with larger data rate requirements. While TDMA as describedabove may be employed in one embodiment of the invention, a TDMAapproach may require time synchronization across different clusters andmay result in lower spectral efficiency since interfering clusterscannot use the same frequency at the same time.

In one embodiment, all neighboring clusters transmit at the same time inthe same frequency band and use spatial processing across clusters toavoid interference. In this embodiment, the multi-cluster DIDO system:(i) uses conventional DIDO precoding within the main cluster to transmitsimultaneous non-interfering data streams within the same frequency bandto multiple clients (such as described in the related patents andapplications, including U.S. Pat. Nos. 7,599,420; 7,633,994; 7,636,381;and application Ser. No. 12/143,503); (ii) uses DIDO precoding withinterference cancellation in the neighbor clusters to avoid interferenceto the clients lying in the interfering zones 8010 in FIG. 80, bycreating points of zero radio frequency (RF) energy at the locations ofthe target clients. If a target client is in an interfering zone 8010,it will receive the sum of the RF containing the data stream from themain cluster 8011 and the zero RF energy from the interfering cluster8012-8013, which will simply be the RF containing the data stream fromthe main cluster. Thus, adjacent clusters can utilize the same frequencysimultaneously without target clients in the interfering zone sufferingfrom interference.

In practical systems, the performance of DIDO precoding may be affectedby different factors such as: channel estimation error or Dopplereffects (yielding obsolete channel state information at the DIDOdistributed antennas); intermodulation distortion (IMD) in multicarrierDIDO systems; time or frequency offsets. As a result of these effects,it may be impractical to achieve points of zero RF energy. However, aslong as the RF energy at the target client from the interfering clustersis negligible compared to the RF energy from the main cluster, the linkperformance at the target client is unaffected by the interference. Forexample, let us assume the client requires 20 dB signal-to-noise ratio(SNR) to demodulate 4-QAM constellations using forward error correction(FEC) coding to achieve target bit error rate (BER) of 10⁻⁶. If the RFenergy at the target client received from the interfering cluster is 20dB below the RF energy received from the main cluster, the interferenceis negligible and the client can demodulate data successfully within thepredefined BER target. Thus, the term “zero RF energy” as used hereindoes not necessarily mean that the RF energy from interfering RF signalsis zero. Rather, it means that the RF energy is sufficiently lowrelative to the RF energy of the desired RF signal such that the desiredRF signal may be received at the receiver. Moreover, while certaindesirable thresholds for interfering RF energy relative to desired RFenergy are described, the underlying principles of the invention are notlimited to any particular threshold values.

There are different types of interfering zones 8010 as shown in FIG. 80.For example, “type A” zones (as indicated by the letter “A” in FIG. 80)are affected by interference from only one neighbor cluster, whereas“type B” zones (as indicated by the letter “B”) account for interferencefrom two or multiple neighbor clusters.

FIG. 81 depicts a framework employed in one embodiment of the invention.The dots denote DIDO distributed antennas, the crosses refer to the DIDOclients and the arrows indicate the directions of propagation of RFenergy. The DIDO antennas in the main cluster transmit precoded datasignals to the clients MC 8101 in that cluster. Likewise, the DIDOantennas in the interfering cluster serve the clients IC 8102 withinthat cluster via conventional DIDO precoding. The green cross 8103denotes the target client TC 8103 in the interfering zone. The DIDOantennas in the main cluster 8011 transmit precoded data signals to thetarget client (black arrows) via conventional DIDO precoding. The DIDOantennas in the interfering cluster 8012 use precoding to create zero RFenergy towards the directions of the target client 8103 (green arrows).

The received signal at target client k in any interfering zone 8010A, Bin FIG. 80 is given by

$\begin{matrix}{r_{k} = {{H_{k}W_{k}s_{k}} + {H_{k}{\sum\limits_{\underset{u \neq k}{u = 1}}^{U}{W_{u}s_{u}}}} + {\sum\limits_{c = 1}^{C}{H_{c,k}{\sum\limits_{i = 1}^{I_{C}}{W_{c,i}s_{c,i}}}}} + n_{k}}} & (1)\end{matrix}$where k=1, . . . , K, with K being the number of clients in theinterfering zone 8010A, B, U is the number of clients in the main DIDOcluster, C is the number of interfering DIDO clusters 8012-8013 andI_(c) is the number of clients in the interfering cluster c. Moreover,r_(k) ∈

^(N×M) is the vector containing the receive data streams at client k,assuming M transmit DIDO antennas and N receive antennas at the clientdevices; s_(k) ∈

^(N×1) is the vector of transmit data streams to client k in the mainDIDO cluster; s_(u) ∈

^(N×1) is the vector of transmit data streams to client u in the mainDIDO cluster; s_(c,i) ∈

^(N×1) is the vector of transmit data streams to client i in the c^(th)interfering DIDO cluster; n_(k) ∈

^(N×1) is the vector of additive white Gaussian noise (AWGN) at the Nreceive antennas of client k; H_(k) ∈

^(N×M) is the DIDO channel matrix from the M transmit DIDO antennas tothe N receive antennas at client k in the main DIDO cluster; H_(c,k) ∈

^(N×M) is the DIDO channel matrix from the M transmit DIDO antennas tothe N receive antennas t client k in the c^(th) interfering DIDOcluster; W_(k) ∈

^(M×N) is the matrix of DIDO precoding weights to client k in the mainDIDO cluster; W_(k) ∈

^(M×N) is the matrix of DIDO precoding weights to client u in the mainDIDO cluster; W_(c,i) ∈

^(M×N) is the matrix of DIDO precoding weights to client i in the c^(th)interfering DIDO cluster.

To simplify the notation and without loss of generality, we assume allclients are equipped with N receive antennas and there are M DIDOdistributed antennas in every DIDO cluster, with M≥(N·U) andM≥(N·I_(c)), ∀c=1, . . . , C. If M is larger than the total number ofreceive antennas in the cluster, the extra transmit antennas are used topre-cancel interference to the target clients in the interfering zone orto improve link robustness to the clients within the same cluster viadiversity schemes described in the related patents and applications,including U.S. Pat. Nos. 7,599,420; 7,633,994; 7,636,381; andapplication Ser. No. 12/143,503.

The DIDO precoding weights are computed to pre-cancel inter-clientinterference within the same DIDO cluster. For example, blockdiagonalization (BD) precoding described in the related patents andapplications, including U.S. Pat. Nos. 7,599,420; 7,633,994; 7,636,381;and application Ser. No. 12/143,503 and [7] can be used to removeinter-client interference, such that the following condition issatisfied in the main clusterH _(k) W _(u)=0^(N×N) ; ∀u=1, . . . ,U; with u≠k.  (2)The precoding weight matrices in the neighbor DIDO clusters are designedsuch that the following condition is satisfiedH _(c,k) W _(c,i)=0^(N×N) ; ∀c=1, . . . ,C and ∀i=1, . . . ,I _(c).  (3)To compute the precoding matrices W_(c,i), the downlink channel from theM transmit antennas to the I_(c) clients in the interfering cluster aswell as to client k in the interfering zone is estimated and theprecoding matrix is computed by the DIDO BTS in the interfering cluster.If BD method is used to compute the precoding matrices in theinterfering clusters, the following effective channel matrix is built tocompute the weights to the i^(th) client in the neighbor clusters

$\begin{matrix}{{\overset{\_}{H}}_{c,i} = \begin{bmatrix}H_{c,k} \\{\overset{\sim}{H}}_{c,i}\end{bmatrix}} & (4)\end{matrix}$where {tilde over (H)}_(c,i) is the matrix obtained from the channelmatrix H_(c)∈

^((N−I) ^(c) ^()×M) for the interfering cluster c, where the rowscorresponding to the i^(th) client are removed. Substituting conditions(2) and (3) into (1), we obtain the received data streams for targetclient k, where intra-cluster and inter-cluster interference is removedr _(k) =H _(k) W _(k) s _(k) +n _(k).  (5)The precoding weights W_(c,i) in (1) computed in the neighbor clustersare designed to transmit precoded data streams to all clients in thoseclusters, while pre-cancelling interference to the target client in theinterfering zone. The target client receives precoded data only from itsmain cluster. In a different embodiment, the same data stream is sent tothe target client from both main and neighbor clusters to obtaindiversity gain. In this case, the signal model in (5) is expressed asr _(k)=(H _(k) W _(k)+Σ_(c=1) ^(C) H _(c,k) W _(c,k))s _(k) +n _(k)  (6)where W_(c,k) is the DIDO precoding matrix from the DIDO transmitters inthe c^(th) cluster to the target client k in the interfering zone. Notethat the method in (6) requires time synchronization across neighboringclusters, which may be complex to achieve in large systems, butnonetheless, is quite feasible if the diversity gain benefit justifiesthe cost of implementation.

We begin by evaluating the performance of the proposed method in termsof symbol error rate (SER) as a function of the signal-to-noise ratio(SNR). Without loss of generality, we define the following signal modelassuming single antenna per client and reformulate (1) asr _(k) =√{square root over (SNR)}+h _(k) w _(k) s _(k) +√{square rootover (INR)}h _(c,k)Σ_(i=1) ^(I) w _(c,i) s _(c,i) +n _(k)  (7)where INR is the interference-to-noise ratio defined as INR=SNR/SIR andSIR is the signal-to-interference ratio.

FIG. 82 shows the SER as a function of the SNR, assuming SIR=10 dB forthe target client in the interfering zone. Without loss of generality,we measured the SER for 4-QAM and 16-QAM without forwards errorcorrection (FEC) coding. We fix the target SER to 1% for uncodedsystems. This target corresponds to different values of SNR depending onthe modulation order (i.e., SNR=20 dB for 4-QAM and SNR=28 dB for16-QAM). Lower SER targets can be satisfied for the same values of SNRwhen using FEC coding due to coding gain. We consider the scenario oftwo clusters (one main cluster and one interfering cluster) with twoDIDO antennas and two clients (equipped with single antenna each) percluster. One of the clients in the main cluster lies in the interferingzone. We assume flat-fading narrowband channels, but the followingresults can be extended to frequency selective multicarrier (OFDM)systems, where each subcarrier undergoes flat-fading. We consider twoscenarios: (i) one with inter-DIDO-cluster interference (IDCI) where theprecoding weights w_(c,i) are computed without accounting for the targetclient in the interfering zone; and (ii) the other where the IDCI isremoved by computing the weights w_(c,i) to cancel IDCI to the targetclient. We observe that in presence of IDCI the SER is high and abovethe predefined target. With IDCI-precoding at the neighbor cluster theinterference to the target client is removed and the SER targets arereached for SNR>20 dB.

The results in FIG. 82 assumes IDCI-precoding as in (5). IfIDCI-precoding at the neighbor clusters is also used to precode datastreams to the target client in the interfering zone as in (6),additional diversity gain is obtained. FIG. 83 compares the SER derivedfrom two techniques: (i) “Method 1” using the IDCI-precoding in (5);(ii) “Method 2” employing IDCI-precoding in (6) where the neighborclusters also transmit precoded data stream to the target client. Method2 yields ˜3 dB gain compared to conventional IDCI-precoding due toadditional array gain provided by the DIDO antennas in the neighborcluster used to transmit precoded data stream to the target client. Moregenerally, the array gain of Method 2 over Method 1 is proportional to10*log 10(C+1), where C is the number of neighbor clusters and thefactor “1” refers to the main cluster.

Next, we evaluate the performance of the above method as a function ofthe target client's location with respect to the interfering zone. Weconsider one simple scenario where a target client 8401 moves from themain DIDO cluster 8402 to the interfering cluster 8403, as depicted inFIG. 84. We assume all DIDO antennas 8412 within the main cluster 8402employ BD precoding to cancel intra-cluster interference to satisfycondition (2). We assume single interfering DIDO cluster, singlereceiver antenna at the client device 8401 and equal pathloss from allDIDO antennas in the main or interfering cluster to the client (i.e.,DIDO antennas placed in circle around the client). We use one simplifiedpathloss model with pathloss exponent 4 (as in typical urbanenvironments) [11].

The analysis hereafter is based on the following simplified signal modelthat extends (7) to account for pathloss

$\begin{matrix}{r_{k} = {{\sqrt{\frac{{SNR} \cdot D_{o}^{4}}{D^{4}}}h_{k}w_{k}s_{k}} + {\sqrt{\frac{{SNR} \cdot D_{o}^{4}}{\left( {1 - D} \right)^{4}}}h_{c,k}{\sum\limits_{i = 1}^{I}{w_{c,i}s_{c,i}}}} + n_{k}}} & (8)\end{matrix}$where the signal-to-interference (SIR) is derived as SIR=((1−D)/D)⁴. Inmodeling the IDCI, we consider three scenarios: i) ideal case with noIDCI; ii) IDCI pre-cancelled via BD precoding in the interfering clusterto satisfy condition (3); iii) with IDCI, not pre-cancelled by theneighbor cluster.

FIG. 85 shows the signal-to-interference-plus-noise ratio (SINR) as afunction of D (i.e., as the target client moves from the main cluster8402 towards the DIDO antennas 8413 in the interfering cluster 8403).The SINR is derived as the ratio of signal power and interference plusnoise power using the signal model in (8). We assume that D_(o)=0.1 andSNR=50 dB for D=D_(o). In absence of IDCI the wireless link performanceis only affected by noise and the SINR decreases due to pathloss. Inpresence of IDCI (i.e., without IDCI-precoding) the interference fromthe DIDO antennas in the neighbor cluster contributes to reduce theSINR.

FIG. 86 shows the symbol error rate (SER) performance of the threescenarios above for 4-QAM modulation in flat-fading narrowband channels.These SER results correspond to the SINR in FIG. 85. We assume SERthreshold of 1% for uncoded systems (i.e., without FEC) corresponding toSINR threshold SINR_(T)=20 dB in FIG. 85. The SINR threshold depends onthe modulation order used for data transmission. Higher modulationorders are typically characterized by higher SINR_(T) to achieve thesame target error rate. With FEC, lower target SER can be achieved forthe same SINR value due to coding gain. In case of IDCI withoutprecoding, the target SER is achieved only within the range D<0.25. WithIDCI-precoding at the neighbor cluster the range that satisfies thetarget SER is extended up to D<0.6. Beyond that range, the SINRincreases due to pathloss and the SER target is not satisfied.

One embodiment of a method for IDCI precoding is shown in FIG. 87 andconsists of the following steps:

-   -   SIR estimate 8701: Clients estimate the signal power from the        main DIDO cluster (i.e., based on received precoded data) and        the interference-plus-noise signal power from the neighbor DIDO        clusters. In single-carrier DIDO systems, the frame structure        can be designed with short periods of silence. For example,        periods of silence can be defined between training for channel        estimation and precoded data transmissions during channel state        information (CSI) feedback. In one embodiment, the        interference-plus-noise signal power from neighbor clusters is        measured during the periods of silence from the DIDO antennas in        the main cluster. In practical DIDO multicarrier (OFDM) systems,        null tones are typically used to prevent direct current (DC)        offset and attenuation at the edge of the band due to filtering        at transmit and receive sides. In another embodiment employing        multicarrier systems, the interference-plus-noise signal power        is estimated from the null tones. Correction factors can be used        to compensate for transmit/receive filter attenuation at the        edge of the band. Once the signal-plus-interference-and-noise        power (P_(S)) from the main cluster and the        interference-plus-noise power from neighbor clusters (P_(IN))        are estimated, the client computes the SINR as

$\begin{matrix}{{SINR} = {\frac{P_{S} - P_{IN}}{P_{IN}}.}} & (9)\end{matrix}$

-   -   Alternatively, the SINR estimate is derived from the received        signal strength indication (RSSI) used in typical wireless        communication systems to measure the radio signal power.    -   We observe the metric in (9) cannot discriminate between noise        and interference power level. For example, clients affected by        shadowing (i.e., behind obstacles that attenuate the signal        power from all DIDO distributed antennas in the main cluster) in        interference-free environments may estimate low SINR even though        they are not affected by inter-cluster interference. A more        reliable metric for the proposed method is the SIR computed as

$\begin{matrix}{{SIR} = \frac{P_{S} - P_{IN}}{P_{IN} - P_{N}}} & (10)\end{matrix}$

-   -   where P_(N) is the noise power. In practical multicarrier OFDM        systems, the noise power P_(N) in (10) is estimated from the        null tones, assuming all DIDO antennas from main and neighbor        clusters use the same set of null tones. The        interference-plus-noise power (P_(IN)), is estimated from the        period of silence as mentioned above. Finally, the        signal-plus-interference-and-noise power (P_(S)) is derived from        the data tones. From these estimates, the client computes the        SIR in (10).    -   Channel estimation at neighbor clusters 8702-8703: If the        estimated SIR in (10) is below predefined threshold (SIR_(T)),        determined at 8702 in FIG. 87, the client starts listening to        training signals from neighbor clusters. Note that SIR_(T)        depends on the modulation and FEC coding scheme (MCS) used for        data transmission. Different SIR targets are defined depending        on the client's MCS. When DIDO distributed antennas from        different clusters are time-synchronized (i.e., locked to the        same pulse-per-second, PPS, time reference), the client exploits        the training sequence to deliver its channel estimates to the        DIDO antennas in the neighbor clusters at 8703. The training        sequence for channel estimation in the neighbor clusters are        designed to be orthogonal to the training from the main cluster.        Alternatively, when DIDO antennas in different clusters are not        time-synchronized, orthogonal sequences (with good        cross-correlation properties) are used for time synchronization        in different DIDO clusters. Once the client locks to the        time/frequency reference of the neighbor clusters, channel        estimation is carried out at 8703.    -   IDCI Precoding 8704: Once the channel estimates are available at        the DIDO BTS in the neighbor clusters, IDCI-precoding is        computed to satisfy the condition in (3). The DIDO antennas in        the neighbor clusters transmit precoded data streams only to the        clients in their cluster, while pre-cancelling interference to        the clients in the interfering zone 8010 in FIG. 80. We observe        that if the client lies in the type B interfering zone 8010 in        FIG. 80, interference to the client is generated by multiple        clusters and IDCI-precoding is carried out by all neighbor        clusters at the same time.        Methods for Handoff

Hereafter, we describe different handoff methods for clients that moveacross DIDO clusters populated by distributed antennas that are locatedin separate areas or that provide different kinds of services (i.e.,low- or high-mobility services).

a. Handoff Between Adjacent DIDO Clusters

In one embodiment, the IDCI-precoder to remove inter-clusterinterference described above is used as a baseline for handoff methodsin DIDO systems. Conventional handoff in cellular systems is conceivedfor clients to switch seamlessly across cells served by different basestations. In DIDO systems, handoff allows clients to move from onecluster to another without loss of connection.

To illustrate one embodiment of a handoff strategy for DIDO systems, weconsider again the example in FIG. 84 with only two clusters 8402 and8403. As the client 8401 moves from the main cluster (C1) 8402 to theneighbor cluster (C2) 8403, one embodiment of a handoff methoddynamically calculates the signal quality in different clusters andselects the cluster that yields the lowest error rate performance to theclient.

FIG. 88 shows the SINR variation as a function of the client's distancefrom the center of clusters C1. For 4-QAM modulation without FEC coding,we consider target SINR=20 dB. The line identified by circles representsthe SINR for the target client being served by the DIDO antennas in C1,when both C1 and C2 use DIDO precoding without interferencecancellation. The SINR decreases as a function of D due to pathloss andinterference from the neighboring cluster. When IDCI-precoding isimplemented at the neighboring cluster, the SINR loss is only due topathloss (as shown by the line with triangles), since interference iscompletely removed. Symmetric behavior is experienced when the client isserved from the neighboring cluster. One embodiment of the handoffstrategy is defined such that, as the client moves from C1 to C2, thealgorithm switches between different DIDO schemes to maintain the SINRabove predefined target.

From the plots in FIG. 88, we derive the SER for 4-QAM modulation inFIG. 89. We observe that, by switching between different precodingstrategies, the SER is maintained within predefined target.

One embodiment of the handoff strategy is as follows.

-   -   C1-DIDO and C2-DIDO precoding: When the client lies within C1,        away from the interfering zone, both clusters C1 and C2 operate        with conventional DIDO precoding independently.    -   C1-DIDO and C2-IDCI precoding: As the client moves towards the        interfering zone, its SIR or SINR degrades. When the target        SINR_(T1) is reached, the target client starts estimating the        channel from all DIDO antennas in C2 and provides the CSI to the        BTS of C2. The BTS in C2 computes IDCI-precoding and transmits        to all clients in C2 while preventing interference to the target        client. For as long as the target client is within the        interfering zone, it will continue to provide its CSI to both C1        and C2.    -   C1-IDCI and C2-DIDO precoding: As the client moves towards C2,        its SIR or SINR keeps decreasing until it again reaches a        target. At this point the client decides to switch to the        neighbor cluster. In this case, C1 starts using the CSI from the        target client to create zero interference towards its direction        with IDCI-precoding, whereas the neighbor cluster uses the CSI        for conventional DIDO-precoding. In one embodiment, as the SIR        estimate approaches the target, the clusters C1 and C2 try both        DIDO- and IDCI-precoding schemes alternatively, to allow the        client to estimate the SIR in both cases. Then the client        selects the best scheme, to maximize certain error rate        performance metric. When this method is applied, the cross-over        point for the handoff strategy occurs at the intersection of the        curves with triangles and rhombus in FIG. 88. One embodiment        uses the modified IDCI-precoding method described in (6) where        the neighbor cluster also transmits precoded data stream to the        target client to provide array gain. With this approach the        handoff strategy is simplified, since the client does not need        to estimate the SINR for both strategies at the cross-over        point.    -   C1-DIDO and C2-DIDO precoding: As the client moves out of the        interference zone towards C2, the main cluster C1 stops        pre-cancelling interference towards that target client via        IDCI-precoding and switches back to conventional DIDO-precoding        to all clients remaining in C1. This final cross-over point in        our handoff strategy is useful to avoid unnecessary CSI feedback        from the target client to C1, thereby reducing the overhead over        the feedback channel. In one embodiment a second target        SINR_(T2) is defined. When the SINR (or SIR) increases above        this target, the strategy is switched to C1-DIDO and C2-DIDO. In        one embodiment, the cluster C1 keeps alternating between DIDO-        and IDCI-precoding to allow the client to estimate the SINR.        Then the client selects the method for C1 that more closely        approaches the target SINR_(T1) from above.

The method described above computes the SINR or SIR estimates fordifferent schemes in real time and uses them to select the optimalscheme. In one embodiment, the handoff algorithm is designed based onthe finite-state machine illustrated in FIG. 90. The client keeps trackof its current state and switches to the next state when the SINR or SIRdrops below or above the predefined thresholds illustrated in FIG. 88.As discussed above, in state 8801, both clusters C1 and C2 operate withconventional DIDO precoding independently and the client is served bycluster C1; in state 8802, the client is served by cluster C1, the BTSin C2 computes IDCI-precoding and cluster C1 operates using conventionalDIDO precoding; in state 8803, the client is served by cluster C2, theBTS in C1 computes IDCI-precoding and cluster C2 operates usingconventional DIDO precoding; and in state 8804, the client is served bycluster C2, and both clusters C1 and C2 operate with conventional DIDOprecoding independently.

In presence of shadowing effects, the signal quality or SIR mayfluctuate around the thresholds as shown in FIG. 91, causing repetitiveswitching between consecutive states in FIG. 90. Changing statesrepetitively is an undesired effect, since it results in significantoverhead on the control channels between clients and BTSs to enableswitching between transmission schemes. FIG. 91 depicts one example of ahandoff strategy in the presence of shadowing. In one embodiment, theshadowing coefficient is simulated according to log-normal distributionwith variance 3 [3]. Hereafter, we define some methods to preventrepetitive switching effect during DIDO handoff.

One embodiment of the invention employs a hysteresis loop to cope withstate switching effects. For example, when switching between“C1-DIDO,C2-IDCI” 9302 and “C1-IDCI,C2-DIDO” 9303 states in FIG. 90 (orvice versa) the threshold SINR_(T1) can be adjusted within the range A₁.This method avoids repetitive switches between states as the signalquality oscillates around SINR_(T1). For example, FIG. 92 shows thehysteresis loop mechanism when switching between any two states in FIG.90. To switch from state B to A the SIR must be larger than(SIR_(T1)+A₁/2), but to switch back from A to B the SIR must drop below(SIR_(T1)−A₁/2).

In a different embodiment, the threshold SINR_(T2) is adjusted to avoidrepetitive switching between the first and second (or third and fourth)states of the finite-state machine in FIG. 90. For example, a range ofvalues A₂ may be defined such that the threshold SINR_(T2) is chosenwithin that range depending on channel condition and shadowing effects.

In one embodiment, depending on the variance of shadowing expected overthe wireless link, the SINR threshold is dynamically adjusted within therange [SINR_(T2), SINR_(T2)+A₂]. The variance of the log-normaldistribution can be estimated from the variance of the received signalstrength (or RSSI) as the client moves from its current cluster to theneighbor cluster.

The methods above assume the client triggers the handoff strategy. Inone embodiment, the handoff decision is deferred to the DIDO BTSs,assuming communication across multiple BTSs is enabled.

For simplicity, the methods above are derived assuming no FEC coding and4-QAM. More generally, the SINR or SIR thresholds are derived fordifferent modulation coding schemes (MCSs) and the handoff strategy isdesigned in combination with link adaptation (see, e.g., U.S. Pat. No.7,636,381) to optimize downlink data rate to each client in theinterfering zone.

b. Handoff Between Low- and High-Doppler DIDO Networks

DIDO systems employ closed-loop transmission schemes to precode datastreams over the downlink channel. Closed-loop schemes are inherentlyconstrained by latency over the feedback channel. In practical DIDOsystems, computational time can be reduced by transceivers with highprocessing power and it is expected that most of the latency isintroduced by the DIDO BSN, when delivering CSI and baseband precodeddata from the BTS to the distributed antennas. The BSN can be comprisedof various network technologies including, but not limited to, digitalsubscriber lines (DSL), cable modems, fiber rings, T1 lines, hybridfiber coaxial (HFC) networks, and/or fixed wireless (e.g., WiFi).Dedicated fiber typically has very large bandwidth and low latency,potentially less than a millisecond in local region, but it is lesswidely deployed than DSL and cable modems. Today, DSL and cable modemconnections typically have between 10-25 ms in last-mile latency in theUnited States, but they are very widely deployed.

The maximum latency over the BSN determines the maximum Dopplerfrequency that can be tolerated over the DIDO wireless link withoutperformance degradation of DIDO precoding. For example, in [1] we showedthat at the carrier frequency of 400 MHz, networks with latency of about10 msec (i.e., DSL) can tolerate clients' velocity up to 8 mph (runningspeed), whereas networks with 1 msec latency (i.e., fiber ring) cansupport speed up to 70 mph (i.e., freeway traffic).

We define two or multiple DIDO sub-networks depending on the maximumDoppler frequency that can be tolerated over the BSN. For example, a BSNwith high-latency DSL connections between the DIDO BTS and distributedantennas can only deliver low mobility or fixed-wireless services (i.e.,low-Doppler network), whereas a low-latency BSN over a low-latency fiberring can tolerate high mobility (i.e., high-Doppler network). We observethat the majority of broadband users are not moving when they usebroadband, and further, most are unlikely to be located near areas withmany high speed objects moving by (e.g., next to a highway) since suchlocations are typically less desirable places to live or operate anoffice. However, there are broadband users who will be using broadbandat high speeds (e.g., while in a car driving on the highway) or will benear high speed objects (e.g., in a store located near a highway). Toaddress these two differing user Doppler scenarios, in one embodiment, alow-Doppler DIDO network consists of a typically larger number of DIDOantennas with relatively low power (i.e., 1 W to 100 W, for indoor orrooftop installation) spread across a wide area, whereas a high-Dopplernetwork consists of a typically lower number of DIDO antennas with highpower transmission (i.e., 100 W for rooftop or tower installation). Thelow-Doppler DIDO network serves the typically larger number oflow-Doppler users and can do so at typically lower connectivity costusing inexpensive high-latency broadband connections, such as DSL andcable modems. The high-Doppler DIDO network serves the typically fewernumber of high-Doppler users and can do so at typically higherconnectivity cost using more expensive low-latency broadbandconnections, such as fiber.

To avoid interference across different types of DIDO networks (e.g.low-Doppler and high-Doppler), different multiple access techniques canbe employed such as: time division multiple access (TDMA), frequencydivision multiple access (FDMA), or code division multiple access(CDMA).

Hereafter, we propose methods to assign clients to different types ofDIDO networks and enable handoff between them. The network selection isbased on the type of mobility of each client. The client's velocity (v)is proportional to the maximum Doppler shift according to the followingequation [6]

$\begin{matrix}{f_{d} = {\frac{v}{\lambda}\sin\;\theta}} & (11)\end{matrix}$where f_(d) is the maximum Doppler shift, λ is the wavelengthcorresponding to the carrier frequency and θ is the angle between thevector indicating the direction transmitter-client and the velocityvector.

In one embodiment, the Doppler shift of every client is calculated viablind estimation techniques. For example, the Doppler shift can beestimated by sending RF energy to the client and analyzing the reflectedsignal, similar to Doppler radar systems.

In another embodiment, one or multiple DIDO antennas send trainingsignals to the client. Based on those training signals, the clientestimates the Doppler shift using techniques such as counting thezero-crossing rate of the channel gain, or performing spectrum analysis.We observe that for fixed velocity v and client's trajectory, theangular velocity v sin θ in (11) may depend on the relative distance ofthe client from every DIDO antenna. For example, DIDO antennas in theproximity of a moving client yield larger angular velocity and Dopplershift than faraway antennas. In one embodiment, the Doppler velocity isestimated from multiple DIDO antennas at different distances from theclient and the average, weighted average or standard deviation is usedas an indicator for the client's mobility. Based on the estimatedDoppler indicator, the DIDO BTS decides whether to assign the client tolow- or high-Doppler networks.

The Doppler indicator is periodically monitored for all clients and sentback to the BTS. When one or multiple clients change their Dopplervelocity (i.e., client riding in the bus versus client walking orsitting), those clients are dynamically re-assigned to different DIDOnetwork that can tolerate their level of mobility.

Although the Doppler of low-velocity clients can be affected by being inthe vicinity of high-velocity objects (e.g. near a highway), the Doppleris typically far less than the Doppler of clients that are in motionthemselves. As such, in one embodiment, the velocity of the client isestimated (e.g. by using a means such as monitoring the clients positionusing GPS), and if the velocity is low, the client is assigned to alow-Doppler network, and if the velocity if high, the client is assignedto a high-Doppler network.

Methods for Power Control and Antenna Grouping

The block diagram of DIDO systems with power control is depicted in FIG.93. One or multiple data streams (s_(k)) for every client (1, . . . , U)are first multiplied by the weights generated by the DIDO precodingunit. Precoded data streams are multiplied by power scaling factorcomputed by the power control unit, based on the input channel qualityinformation (CQI). The CQI is either fed back from the clients to DIDOBTS or derived from the uplink channel assuming uplink-downlink channelreciprocity. The U precoded streams for different clients are thencombined and multiplexed into M data streams (t_(m)), one for each ofthe M transmit antennas. Finally, the streams t_(m) are sent to thedigital-to-analog converter (DAC) unit, the radio frequency (RF) unit,power amplifier (PA) unit and finally to the antennas.

The power control unit measures the CQI for all clients. In oneembodiment, the CQI is the average SNR or RSSI. The CQI varies fordifferent clients depending on pathloss or shadowing. Our power controlmethod adjusts the transmit power scaling factors P_(k) for differentclients and multiplies them by the precoded data streams generated fordifferent clients. Note that one or multiple data streams may begenerated for every client, depending on the number of clients' receiveantennas.

To evaluate the performance of the proposed method, we defined thefollowing signal model based on (5), including pathloss and powercontrol parametersr _(k)=√{square root over (SNR P_(k)α_(k))}H _(k) W _(k) s _(k) +n_(k)  (12)where k=1, . . . , U, U is the number of clients, SNR=P_(o)/N_(o), withP_(o) being the average transmit power, N_(o) the noise power and α_(k)the pathloss/shadowing coefficient. To model pathloss/shadowing, we usethe following simplified model

$\begin{matrix}{\alpha_{k} = e^{{- a}\frac{k - 1}{U}}} & (13)\end{matrix}$where a=4 is the pathloss exponent and we assume the pathloss increaseswith the clients' index (i.e., clients are located at increasingdistance from the DIDO antennas).

FIG. 94 shows the SER versus SNR assuming four DIDO transmit antennasand four clients in different scenarios. The ideal case assumes allclients have the same pathloss (i.e., a=0), yielding P_(k)=1 for allclients. The plot with squares refers to the case where clients havedifferent pathloss coefficients and no power control. The curve withdots is derived from the same scenario (with pathloss) where the powercontrol coefficients are chosen such that P_(k)=1/α_(k). With the powercontrol method, more power is assigned to the data streams intended tothe clients that undergo higher pathloss/shadowing, resulting in 9 dBSNR gain (for this particular scenario) compared to the case with nopower control.

The Federal Communications Commission (FCC) (and other internationalregulatory agencies) defines constraints on the maximum power that canbe transmitted from wireless devices to limit the exposure of human bodyto electromagnetic (EM) radiation. There are two types of limits [2]: i)“occupational/controlled” limit, where people are made fully aware ofthe radio frequency (RF) source via fences, warnings or labels; ii)“general population/uncontrolled” limit where there is no control overthe exposure.

Different emission levels are defined for different types of wirelessdevices. In general, DIDO distributed antennas used for indoor/outdoorapplications qualify for the FCC category of “mobile” devices, definedas [2]:

“transmitting devices designed to be used in other than fixed locationsthat would normally be used with radiating structures maintained 20 cmor more from the body of the user or nearby persons.”

The EM emission of “mobile” devices is measured in terms of maximumpermissible exposure (MPE), expressed in mW/cm². FIG. 95 shows the MPEpower density as a function of distance from the source of RF radiationfor different values of transmit power at 700 MHz carrier frequency. Themaximum allowed transmit power to meet the FCC “uncontrolled” limit fordevices that typically operate beyond 20 cm from the human body is 1 W.

Less restrictive power emission constraints are defined for transmittersinstalled on rooftops or buildings, away from the “general population”.For these “rooftop transmitters” the FCC defines a looser emission limitof 1000 W, measured in terms of effective radiated power (ERP).

Based on the above FCC constraints, in one embodiment we define twotypes of DIDO distributed antennas for practical systems:

-   -   Low-power (LP) transmitters: located anywhere (i.e., indoor or        outdoor) at any height, with maximum transmit power of 1 W and 5        Mbps consumer-grade broadband (e.g. DSL, cable modem, Fibe To        The Home (FTTH)) backhaul connectivity.    -   High-power (HP) transmitters: rooftop or building mounted        antennas at height of approximately 10 meters, with transmit        power of 100 W and a commercial-grade broadband (e.g. optical        fiber ring) backhaul (with effectively “unlimited” data rate        compared to the throughput available over the DIDO wireless        links).

Note that LP transmitters with DSL or cable modem connectivity are goodcandidates for low-Doppler DIDO networks (as described in the previoussection), since their clients are mostly fixed or have low mobility. HPtransmitters with commercial fiber connectivity can tolerate higherclient's mobility and can be used in high-Doppler DIDO networks.

To gain practical intuition on the performance of DIDO systems withdifferent types of LP/HP transmitters, we consider the practical case ofDIDO antenna installation in downtown Palo Alto, Calif. FIG. 96a shows arandom distribution of N_(LP)=100 low-power DIDO distributed antennas inPalo Alto. In FIG. 96 b, 50 LP antennas are substituted with N_(HP)=50high-power transmitters.

Based on the DIDO antenna distributions in FIGS. 96a-b , we derive thecoverage maps in Palo Alto for systems using DIDO technology. FIGS. 97aand 97b show two power distributions corresponding to the configurationsin FIG. 96a and FIG. 96b , respectively. The received power distribution(expressed in dBm) is derived assuming the pathloss/shadowing model forurban environments defined by the 3GPP standard [3] at the carrierfrequency of 700 MHz. We observe that using 50% of HP transmittersyields better coverage over the selected area.

FIGS. 98a-b depict the rate distribution for the two scenarios above.The throughput (expressed in Mbps) is derived based on power thresholdsfor different modulation coding schemes defined in the 3GPP long-termevolution (LTE) standard in [4,5]. The total available bandwidth isfixed to 10 MHz at 700 MHz carrier frequency. Two different frequencyallocation plans are considered: i) 5 MHz spectrum allocated only to theLP stations; ii) 9 MHz to HP transmitters and 1 MHz to LP transmitters.Note that lower bandwidth is typically allocated to LP stations due totheir DSL backhaul connectivity with limited throughput. FIGS. 98a-bshows that when using 50% of HP transmitters it is possible to increasesignificantly the rate distribution, raising the average per-client datarate from 2.4 Mbps in FIGS. 98a to 38M bps in FIG. 98 b.

Next, we defined algorithms to control power transmission of LP stationssuch that higher power is allowed at any given time, thereby increasingthe throughput over the downlink channel of DIDO systems in FIG. 98b .We observe that the FCC limits on the power density is defined based onaverage over time as [2]

$\begin{matrix}{S = \frac{\sum\limits_{n = 1}^{N}{s_{n}t_{n}}}{T_{MPE}}} & (14)\end{matrix}$where T_(MPE)=Σ_(n=1) ^(N)t_(n) is the MPE averaging time, t_(n) is theperiod of time of exposure to radiation with power density S_(n). For“controlled” exposure the average time is 6 minutes, whereas for“uncontrolled” exposure it is increased up to 30 minutes. Then, anypower source is allowed to transmit at larger power levels than the MPElimits, as long as the average power density in (14) satisfies the FCClimit over 30 minute average for “uncontrolled” exposure.

Based on this analysis, we define adaptive power control methods toincrease instantaneous per-antenna transmit power, while maintainingaverage power per DIDO antenna below MPE limits. We consider DIDOsystems with more transmit antennas than active clients. This is areasonable assumption given that DIDO antennas can be conceived asinexpensive wireless devices (similar to WiFi access points) and can beplaced anywhere there is DSL, cable modem, optical fiber, or otherInternet connectivity.

The framework of DIDO systems with adaptive per-antenna power control isdepicted in FIG. 99. The amplitude of the digital signal coming out ofthe multiplexer 994 is dynamically adjusted with power scaling factorsS₁, . . . , S_(m), before being sent to the DAC units 995. The powerscaling factors are computed by the power control unit 992 based on theCQI 993.

In one embodiment, N_(g) DIDO antenna groups are defined. Every groupcontains at least as many DIDO antennas as the number of active clients(K). At any given time, only one group has N_(a)>K active DIDO antennastransmitting to the clients at larger power level (S_(o)) than MPE limit(MPE). One method iterates across all antenna groups according toRound-Robin scheduling policy depicted in FIG. 100. In anotherembodiment, different scheduling techniques (i.e., proportional-fairscheduling [8]) are employed for cluster selection to optimize errorrate or throughput performance.

Assuming Round-Robin power allocation, from (14) we derive the averagetransmit power for every DIDO antenna as

$\begin{matrix}{S = {{S_{o}\frac{t_{o}}{T_{MPE}}} \leq \overset{\_}{MPE}}} & (15)\end{matrix}$where t_(o) is the period of time over which the antenna group is activeand T_(MPE)=30 min is the average time defined by the FCC guidelines[2]. The ratio in (15) is the duty factor (DF) of the groups, definedsuch that the average transmit power from every DIDO antenna satisfiesthe MPE limit (MPE). The duty factor depends on the number of activeclients, the number of groups and active antennas per-group, accordingto the following definition

$\begin{matrix}{{DF}\overset{\Delta}{=}{\frac{K}{N_{g}N_{a}} = {\frac{t_{o}}{T_{MPE}}.}}} & (16)\end{matrix}$The SNR gain (in dB) obtained in DIDO systems with power control andantenna grouping is expressed as a function of the duty factor as

$\begin{matrix}{G_{d\; B} = {10\mspace{14mu}{{\log_{10}\left( \frac{1}{DF} \right)}.}}} & (17)\end{matrix}$We observe the gain in (17) is achieved at the expense of G_(dB)additional transmit power across all DIDO antennas.In general, the total transmit power from all N_(a) of all N_(g) groupsis defined asP=Σ _(j=1) ^(N) ^(g) Σ_(i=1) ^(N) ^(a) P _(ij)  (18)where the P_(ij) is the average per-antenna transmit power given by

$\begin{matrix}{P_{ij} = {{\frac{1}{T_{MPE}}{\int_{0}^{T_{MPE}}{{S_{ij}(t)}d\; t}}} \leq \overset{\_}{MPE}}} & (19)\end{matrix}$and S_(ij)(t) is the power spectral density for the i^(th) transmitantenna within the j^(th) group. In one embodiment, the power spectraldensity in (19) is designed for every antenna to optimize error rate orthroughput performance.

To gain some intuition on the performance of the proposed method,consider 400 DIDO distributed antennas in a given coverage area and 400clients subscribing to a wireless Internet service offered over DIDOsystems. It is unlikely that every Internet connection will be fullyutilized all the time. Let us assume that 10% of the clients will beactively using the wireless Internet connection at any given time. Then,400 DIDO antennas can be divided in N_(g)=10 groups of N_(a)=40 antennaseach, every group serving K=40 active clients at any given time withduty factor DF=0.1. The SNR gain resulting from this transmission schemeis G_(dB)=10 log₁₀(1/DF)=10 dB, provided by 10 dB additional transmitpower from all DIDO antennas. We observe, however, that the averageper-antenna transmit power is constant and is within the MPE limit.

FIG. 101 compares the (uncoded) SER performance of the above powercontrol with antenna grouping against conventional eigenmode selectionin U.S. Pat. No. 7,636,381. All schemes use BD precoding with fourclients, each client equipped with single antenna. The SNR refers to theratio of per-transmit-antenna power over noise power (i.e., per-antennatransmit SNR). The curve denoted with DIDO 4×4 assumes four transmitantenna and BD precoding. The curve with squares denotes the SERperformance with two extra transmit antennas and BD with eigenmodeselection, yielding 10 dB SNR gain (at 1% SER target) over conventionalBD precoding. Power control with antenna grouping and DF=1/10 yields 10dB gain at the same SER target as well. We observe that eigenmodeselection changes the slope of the SER curve due to diversity gain,whereas our power control method shifts the SER curve to the left(maintaining the same slope) due to increased average transmit power.For comparison, the SER with larger duty factor DF=1/50 is shown toprovide additional 7 dB gain compared to DF=1/10.

Note that our power control may have lower complexity than conventionaleigenmode selection methods. In fact, the antenna ID of every group canbe pre-computed and shared among DIDO antennas and clients via lookuptables, such that only K channel estimates are required at any giventime. For eigenmode selection, (K+2) channel estimates are computed andadditional computational processing is required to select the eigenmodethat minimizes the SER at any given time for all clients.

Next, we describe another method involving DIDO antenna grouping toreduce CSI feedback overhead in some special scenarios. FIG. 102a showsone scenario where clients (dots) are spread randomly in one areacovered by multiple DIDO distributed antennas (crosses). The averagepower over every transmit-receive wireless link can be computed asA={|H| ²}.  (20)where H is the channel estimation matrix available at the DIDO BTS.

The matrices A in FIGS. 102a-c are obtained numerically by averaging thechannel matrices over 1000 instances. Two alternative scenarios aredepicted in FIG. 102b and FIG. 102c , respectively, where clients aregrouped together around a subset of DIDO antennas and receive negligiblepower from DIDO antennas located far away. For example, FIG. 102b showstwo groups of antennas yielding block diagonal matrix A. One extremescenario is when every client is very close to only one transmitter andthe transmitters are far away from one another, such that the power fromall other DIDO antennas is negligible. In this case, the DIDO linkdegenerates in multiple SISO links and A is a diagonal matrix as in FIG.102 c.

In all three scenarios above, the BD precoding dynamically adjusts theprecoding weights to account for different power levels over thewireless links between DIDO antennas and clients. It is convenient,however, to identify multiple groups within the DIDO cluster and operateDIDO precoding only within each group. Our proposed grouping methodyields the following advantages:

-   -   Computational gain: DIDO precoding is computed only within every        group in the cluster. For example, if BD precoding is used,        singular value decomposition (SVD) has complexity O(n³), where n        is the minimum dimension of the channel matrix H. If H can be        reduced to a block diagonal matrix, the SVD is computed for        every block with reduced complexity. In fact, if the channel        matrix is divided into two block matrices with dimensions n₁ and        n₂ such that n=n₁+n₂, the complexity of the SVD is only O(n₁        ³)+O(n₂ ³)<O(n³). In the extreme case, if H is diagonal matrix,        the DIDO link reduce to multiple SISO links and no SVD        calculation is required.    -   Reduced CSI feedback overhead: When DIDO antennas and clients        are divided into groups, in one embodiment, the CSI is computed        from the clients to the antennas only within the same group. In        TDD systems, assuming channel reciprocity, antenna grouping        reduces the number of channel estimates to compute the channel        matrix H. In FDD systems where the CSI is fed back over the        wireless link, antenna grouping further yields reduction of CSI        feedback overhead over the wireless links between DIDO antennas        and clients.        Multiple Access Techniques for the DIDO Uplink Channel

In one embodiment of the invention, different multiple access techniquesare defined for the DIDO uplink channel. These techniques can be used tofeedback the CSI or transmit data streams from the clients to the DIDOantennas over the uplink. Hereafter, we refer to feedback CSI and datastreams as uplink streams.

-   -   Multiple-input multiple-output (MIMO): the uplink streams are        transmitted from the client to the DIDO antennas via open-loop        MIMO multiplexing schemes. This method assumes all clients are        time/frequency synchronized. In one embodiment, synchronization        among clients is achieved via training from the downlink and all        DIDO antennas are assumed to be locked to the same        time/frequency reference clock. Note that variations in delay        spread at different clients may generate jitter between the        clocks of different clients that may affect the performance of        MIMO uplink scheme. After the clients send uplink streams via        MIMO multiplexing schemes, the receive DIDO antennas may use        non-linear (i.e., maximum likelihood, ML) or linear (i.e.,        zeros-forcing, minimum mean squared error) receivers to cancel        co-channel interference and demodulate the uplink streams        individually.    -   Time division multiple access (TDMA): Different clients are        assigned to different time slots. Every client sends its uplink        stream when its time slot is available.    -   Frequency division multiple access (FDMA): Different clients are        assigned to different carrier frequencies. In multicarrier        (OFDM) systems, subsets of tones are assigned to different        clients that transmit the uplink streams simultaneously, thereby        reducing latency.    -   Code division multiple access (CDMA): Every client is assigned        to a different pseudo-random sequence and orthogonality across        clients is achieved in the code domain.

In one embodiment of the invention, the clients are wireless devicesthat transmit at much lower power than the DIDO antennas. In this case,the DIDO BTS defines client sub-groups based on the uplink SNRinformation, such that interference across sub-groups is minimized.Within every sub-group, the above multiple access techniques areemployed to create orthogonal channels in time, frequency, space or codedomains thereby avoiding uplink interference across different clients.

In another embodiment, the uplink multiple access techniques describedabove are used in combination with antenna grouping methods presented inthe previous section to define different client groups within the DIDOcluster.

System and Method for Link Adaptation in DIDO Multicarrier Systems

Link adaptation methods for DIDO systems exploiting time, frequency andspace selectivity of wireless channels were defined in U.S. Pat. No.7,636,381. Described below are embodiments of the invention for linkadaptation in multicarrier (OFDM) DIDO systems that exploittime/frequency selectivity of wireless channels.

We simulate Rayleigh fading channels according to the exponentiallydecaying power delay profile (PDP) or Saleh-Valenzuela model in [9]. Forsimplicity, we assume single-cluster channel with multipath PDP definedasP _(n) =e ^(−βn)  (21)where n=0, . . . , L−1, is the index of the channel tap, L is the numberof channel taps and β=1/σ_(DS) is the PDP exponent that is an indicatorof the channel coherence bandwidth, inverse proportional to the channeldelay spread (σ_(DS)). Low values of β yield frequency-flat channels,whereas high values of β produce frequency selective channels. The PDPin (21) is normalized such that the total average power for all Lchannel taps is unitary

$\begin{matrix}{{\overset{\_}{P}}_{n} = {\frac{P_{n}}{\sum\limits_{i = 0}^{L - 1}P_{i}}.}} & (22)\end{matrix}$FIG. 103 depicts the amplitude of low frequency selective channels(assuming β=1) over delay domain or instantaneous PDP (upper plot) andfrequency domain (lower plot) for DIDO 2×2 systems. The first subscriptindicates the client, the second subscript the transmit antenna. Highfrequency selective channels (with β=0.1) are shown in FIG. 104.

Next, we study the performance of DIDO precoding in frequency selectivechannels. We compute the DIDO precoding weights via BD, assuming thesignal model in (1) that satisfies the condition in (2). We reformulatethe DIDO receive signal model in (5), with the condition in (2), asr _(k) =H _(ek) s _(k) +n _(k).  (23)

where H_(ek)=H_(k)W_(k) is the effective channel matrix for user k. ForDIDO 2×2, with a single antenna per client, the effective channel matrixreduces to one value with a frequency response shown in FIG. 105 and forchannels characterized by high frequency selectivity (e.g., with β=0.1)in FIG. 104. The continuous line in FIG. 105 refers to client 1, whereasthe line with dots refers to client 2. Based on the channel qualitymetric in FIG. 105 we define time/frequency domain link adaptation (LA)methods that dynamically adjust MCSs, depending on the changing channelconditions.

We begin by evaluating the performance of different MCSs in AWGN andRayleigh fading SISO channels. For simplicity, we assume no FEC coding,but the following LA methods can be extended to systems that includeFEC.

FIG. 106 shows the SER for different QAM schemes (i.e., 4-QAM, 16-QAM,64-QAM). Without loss of generality, we assume target SER of 1% foruncoded systems. The SNR thresholds to meet that target SER in AWGNchannels are 8 dB, 15.5 dB and 22 dB for the three modulation schemes,respectively. In Rayleigh fading channels, it is well known the SERperformance of the above modulation schemes is worse than AWGN [13] andthe SNR thresholds are: 18.6 dB, 27.3 dB and 34.1 dB, respectively. Weobserve that DIDO precoding transforms the multi-user downlink channelinto a set of parallel SISO links. Hence, the same SNR thresholds as inFIG. 106 for SISO systems hold for DIDO systems on a client-by-clientbasis. Moreover, if instantaneous LA is carried out, the thresholds inAWGN channels are used.

The key idea of the proposed LA method for DIDO systems is to use lowMCS orders when the channel undergoes deep fades in the time domain orfrequency domain (depicted in FIG. 104) to provide link-robustness.Contrarily, when the channel is characterized by large gain, the LAmethod switches to higher MCS orders to increase spectral efficiency.One contribution of the present application compared to U.S. Pat. No.7,636,381 is to use the effective channel matrix in (23) and in FIG. 105as a metric to enable adaptation.

The general framework of the LA methods is depicted in FIG. 107 anddefined as follows:

-   -   CSI estimation: At 1071 the DIDO BTS computes the CSI from all        users. Users may be equipped with single or multiple receive        antennas.    -   DIDO precoding: At 1072, the BTS computes the DIDO precoding        weights for all users. In one embodiment, BD is used to compute        these weights. The precoding weights are calculated on a        tone-by-tone basis.    -   Link-quality metric calculation: At 1073 the BTS computes the        frequency-domain link quality metrics. In OFDM systems, the        metrics are calculated from the CSI and DIDO precoding weights        for every tone. In one embodiment of the invention, the        link-quality metric is the average SNR over all OFDM tones. We        define this method as LA1 (based on average SNR performance). In        another embodiment, the link quality metric is the frequency        response of the effective channel in (23). We define this method        as LA2 (based on tone-by-tone performance to exploit frequency        diversity). If every client has single antenna, the        frequency-domain effective channel is depicted in FIG. 105. If        the clients have multiple receive antennas, the link-quality        metric is defined as the Frobenius norm of the effective channel        matrix for every tone. Alternatively, multiple link-quality        metrics are defined for every client as the singular values of        the effective channel matrix in (23).    -   Bit-loading algorithm: At 1074, based on the link-quality        metrics, the BTS determines the MCSs for different clients and        different OFDM tones. For LA1 method, the same MCS is used for        all clients and all OFDM tones based on the SNR thresholds for        Rayleigh fading channels in FIG. 106. For LA2, different MCSs        are assigned to different OFDM tones to exploit channel        frequency diversity.    -   Precoded data transmission: At 1075, the BTS transmits precoded        data streams from the DIDO distributed antennas to the clients        using the MCSs derived from the bit-loading algorithm. One        header is attached to the precoded data to communicate the MCSs        for different tones to the clients. For example, if eight MCSs        are available and the OFDM symbols are defined with N=64 tone,        log₂(8)*N=192 bits are required to communicate the current MCS        to every client. Assuming 4-QAM (2 bits/symbol spectral        efficiency) is used to map those bits into symbols, only        192/2/N=1.5 OFDM symbols are required to map the MCS        information. In another embodiment, multiple subcarriers (or        OFDM tones) are grouped into subbands and the same MCS is        assigned to all tones in the same subband to reduce the overhead        due to control information. Moreover, the MCS are adjusted based        on temporal variations of the channel gain (proportional to the        coherence time). In fixed-wireless channel (characterized by low        Doppler effect) the MCS are recalculated every fraction of the        channel coherence time, thereby reducing the overhead required        for control information.

FIG. 108 shows the SER performance of the LA methods described above.For comparison, the SER performance in Rayleigh fading channels isplotted for each of the three QAM schemes used. The LA2 method adaptsthe MCSs to the fluctuation of the effective channel in the frequencydomain, thereby providing 1.8 bps/Hz gain in spectral efficiency for lowSNR (i.e., SNR=20 dB) and 15 dB gain in SNR (for SNR>35 dB) compared toLA1.

System and Method for DIDO Precoding Interpolation in MulticarrierSystems

The computational complexity of DIDO systems is mostly localized at thecentralized processor or BTS. The most computationally expensiveoperation is the calculation of the precoding weights for all clientsfrom their CSI. When BD precoding is employed, the BTS has to carry outas many singular value decomposition (SVD) operations as the number ofclients in the system. One way to reduce complexity is throughparallelized processing, where the SVD is computed on a separateprocessor for every client.

In multicarrier DIDO systems, each subcarrier undergoes flat-fadingchannel and the SVD is carried out for every client over everysubcarrier. Clearly the complexity of the system increases linearly withthe number of subcarriers. For example, in OFDM systems with 1 MHzsignal bandwidth, the cyclic prefix (L₀) must have at least eightchannel taps (i.e., duration of 8 microseconds) to avoid intersymbolinterference in outdoor urban macrocell environments with large delayspread [3]. The size (N_(FFT)) of the fast Fourier transform (FFT) usedto generate the OFDM symbols is typically set to multiple of L₀ toreduce loss of data rate. If N_(FFT)=64, the effective spectralefficiency of the system is limited by a factorN_(FFT)/(N_(FFT)+L₀)=89%. Larger values of N_(FFT) yield higher spectralefficiency at the expense of higher computational complexity at the DIDOprecoder.

One way to reduce computational complexity at the DIDO precoder is tocarry out the SVD operation over a subset of tones (that we call pilottones) and derive the precoding weights for the remaining tones viainterpolation. Weight interpolation is one source of error that resultsin inter-client interference. In one embodiment, optimal weightinterpolation techniques are employed to reduce inter-clientinterference, yielding improved error rate performance and lowercomputational complexity in multicarrier systems. In DIDO systems with Mtransmit antennas, U clients and N receive antennas per clients, thecondition for the precoding weights of the k^(th) client (W_(k)) thatguarantees zero interference to the other clients u is derived from (2)asH _(u) W _(k)=0^(N×N) ;∀u=1, . . . ,U; with u≠k  (24)where H_(u) are the channel matrices corresponding to the other DIDOclients in the system.

In one embodiment of the invention, the objective function of the weightinterpolation method is defined as

$\begin{matrix}{{f\left( \theta_{k} \right)} = {\sum\limits_{\underset{u \neq k}{u = 1}}^{U}{{H_{u}{{\hat{W}}_{k}\left( \theta_{k} \right)}}}_{F}}} & (25)\end{matrix}$where θ_(k) is the set of parameters to be optimized for user k,Ŵ_(k)(θ_(k)) is the weight interpolation matrix and ∥.∥_(F) denotes theFrobenius norm of a matrix. The optimization problem is formulated asθ_(k,opt)=arg min_(θ) _(k) _(Θ) _(k) f(θ_(k))  (26)where Θ_(k) is the feasible set of the optimization problem andθ_(k, opt) is the optimal solution.

The objective function in (25) is defined for one OFDM tone. In anotherembodiment of the invention, the objective function is defined as linearcombination of the Frobenius norm in (25) of the matrices for all theOFDM tones to be interpolated. In another embodiment, the OFDM spectrumis divided into subsets of tones and the optimal solution is given byθ_(k,opt)=arg min_(θ) _(k) _(Θ) _(k) max_(nA) f(n,θ _(k))  (27)where n is the OFDM tone index and A is the subset of tones.

The weight interpolation matrix W_(k)(θ_(k)) in (25) is expressed as afunction of a set of parameters θ_(k). Once the optimal set isdetermined according to (26) or (27), the optimal weight matrix iscomputed. In one embodiment of the invention, the weight interpolationmatrix of given OFDM tone n is defined as linear combination of theweight matrices of the pilot tones. One example of weight interpolationfunction for beamforming systems with single client was defined in [11].In DIDO multi-client systems we write the weight interpolation matrix asŴ _(k)(lN ₀ +n,θ _(k))=(1−c _(n))·W(l)+c _(n) e ^(jθ) ^(k) ·W(l+1)  (28)where 0≤l≤(L₀−1), L₀ is the number of pilot tones and c_(n)=(n−1)/N₀,with N₀=N_(FFT)/L₀. The weight matrix in (28) is then normalized suchthat ∥Ŵ_(k)∥_(F)=√{square root over (NM)} to guarantee unitary powertransmission from every antenna. If N=1 (single receive antenna perclient), the matrix in (28) becomes a vector that is normalized withrespect to its norm. In one embodiment of the invention, the pilot tonesare chosen uniformly within the range of the OFDM tones. In anotherembodiment, the pilot tones are adaptively chosen based on the CSI tominimize the interpolation error.

We observe that one key difference of the system and method in [11]against the one proposed in this patent application is the objectivefunction. In particular, the systems in [11] assumes multiple transmitantennas and single client, so the related method is designed tomaximize the product of the precoding weight by the channel to maximizethe receive SNR for the client. This method, however, does not work inmulti-client scenarios, since it yields inter-client interference due tointerpolation error. By contrast, our method is designed to minimizeinter-client interference thereby improving error rate performance toall clients.

FIG. 109 shows the entries of the matrix in (28) as a function of theOFDM tone index for DIDO 2×2 systems with N_(FFT)=64 and L₀=8. Thechannel PDP is generated according to the model in (21) with β=1 and thechannel consists of only eight channel taps. We observe that L₀ must bechosen to be larger than the number of channel taps. The solid lines inFIG. 109 represent the ideal functions, whereas the dotted lines are theinterpolated ones. The interpolated weights match the ideal ones for thepilot tones, according to the definition in (28). The weights computedover the remaining tones only approximate the ideal case due toestimation error.

One way to implement the weight interpolation method is via exhaustivesearch over the feasible set Θ_(k) in (26). To reduce the complexity ofthe search, we quantize the feasible set into P values uniformly in therange [0,2π]. FIG. 110 shows the SER versus SNR for L₀=8, M=N_(t)=2transmit antennas and variable number of P. As the number ofquantization levels increases, the SER performance improves. We observethe case P=10 approaches the performance of P=100 for much lowercomputational complexity, due to reduced number of searches.

FIG. 111 shows the SER performance of the interpolation method fordifferent DIDO orders and L₀=16. We assume the number of clients is thesame as the number of transmit antennas and every client is equippedwith single antenna. As the number of clients increases the SERperformance degrades due to increase inter-client interference producedby weight interpolation errors.

In another embodiment of the invention, weight interpolation functionsother than those in (28) are used. For example, linear predictionautoregressive models [12] can be used to interpolate the weights acrossdifferent OFDM tones, based on estimates of the channel frequencycorrelation.

REFERENCES

-   [1] A. Forenza and S. G. Perlman, “System and method for distributed    antenna wireless communications”, U.S. application Ser. No.    12/630,627, filed Dec. 2, 2009, entitled “System and Method For    Distributed Antenna Wireless Communications”-   [2] FCC, “Evaluating compliance with FCC guidelines for human    exposure to radiofrequency electromagnetic fields,” OET Bulletin 65,    Ed. 97-01, August 1997-   [3] 3GPP, “Spatial Channel Model AHG (Combined ad-hoc from 3GPP &    3GPP2)”, SCM Text V6.0, Apr. 22, 2003-   [4] 3GPP TR 25.912, “Feasibility Study for Evolved UTRA and UTRAN”,    V9.0.0 (2009-10)-   [5] 3GPP TR 25.913, “Requirements for Evolved UTRA (E-UTRA) and    Evolved UTRAN (E-UTRAN)”, V8.0.0 (2009-01)-   [6] W. C. Jakes, Microwave Mobile Communications, IEEE Press, 1974-   [7] K. K. Wong, et al., “A joint channel diagonalization for    multiuser MIMO antenna systems,” IEEE Trans. Wireless Comm., vol. 2,    pp. 773-786, July 2003;-   [8] P. Viswanath, et al., “Opportunistic beamforming using dump    antennas,” IEEE Trans. On Inform. Theory, vol. 48, pp. 1277-1294,    June 2002.-   [9] A. A. M. Saleh, et al., “A statistical model for indoor    multipath propagation,” IEEE Jour. Select. Areas in Comm., vol. 195    SAC-5, no. 2, pp. 128-137, February 1987.-   [10] A. Paulraj, et al., Introduction to Space-Time Wireless    Communications, Cambridge University Press, 40 West 20th Street, New    York, N.Y., USA, 2003.-   [11] J. Choi, et al., “Interpolation Based Transmit Beamforming for    MIMO-OFDM with Limited Feedback,” IEEE Trans. on Signal Processing,    vol. 53, no. 11, pp. 4125-4135, November 2005.-   [12] I. Wong, et al., “Long Range Channel Prediction for Adaptive    OFDM Systems,” Proc. of the IEEE Asilomar Conf. on Signals, Systems,    and Computers, vol. 1, pp. 723-736, Pacific Grove, Calif., USA, Nov.    7-10, 2004.-   [13] J. G. Proakis, Communication System Engineering, Prentice Hall,    1994-   [14] B. D. Van Veen, et al., “Beamforming: a versatile approach to    spatial filtering,” IEEE ASSP Magazine, April 1988.-   [15] R. G. Vaughan, “On optimum combining at the mobile,” IEEE    Trans. On Vehic. Tech., vol 37, n. 4, pp. 181-188, November 1988-   [16] F. Qian, “Partially adaptive beamforming for correlated    interference rejection,” IEEE Trans. On Sign. Proc., vol. 43, n. 2,    pp. 506-515, February 1995-   [17]H. Krim, et. al., “Two decades of array signal processing    research,” IEEE Signal Proc. Magazine, pp. 67-94, July 1996-   [19] W. R. Remley, “Digital beamforming system”,U.S. Pat. No.    4,003,016, January 1977-   [18] R. J. Masak, “Beamforming/null-steering adaptive array”, U.S.    Pat. No. 4,771,289, September 1988-   [20] K.-B.Yu, et. al., “Adaptive digital beamforming architecture    and algorithm for nulling mainlobe and multiple sidelobe radar    jammers while preserving monopulse ratio angle estimation accuracy”,    U.S. Pat. No. 5,600,326, February 1997-   [21]H. Boche, et al., “Analysis of different precoding/decoding    strategies for multiuser beamforming”, IEEE Vehic. Tech. Conf., vol.    1, April 2003-   [22] M. Schubert, et al., “Joint ‘dirty paper’ pre-coding and    downlink beamforming,” vol. 2, pp. 536-540, December 2002-   [23]H. Boche, et al. “A general duality theory for uplink and    downlink beamformingc”, vol. 1, pp. 87-91, December 2002-   [24] K. K. Wong, R. D. Murch, and K. B. Letaief, “A joint channel    diagonalization for multiuser MIMO antenna systems,” IEEE Trans.    Wireless Comm., vol. 2, pp. 773-786, July 2003;-   [25] Q. H. Spencer, A. L. Swindlehurst, and M. Haardt, “Zero forcing    methods for downlink spatial multiplexing in multiuser MIMO    channels,” IEEE Trans. Sig. Proc., vol. 52, pp. 461-471, February    2004.

II. Disclosure from Certain Related Applications

FIG. 1 shows a prior art MIMO system with transmit antennas 104 andreceive antennas 105. Such a system can achieve up to 3× the throughputthat would normally be achievable in the available channel. There are anumber of different approaches in which to implement the details of sucha MIMO system which are described in published literature on thesubject, and the following explanation describes one such approach.

Before data is transmitted in the MIMO system of FIG. 1, the channel is“characterized.” This is accomplished by initially transmitting a“training signal” from each of the transmit antennas 104 to each of thereceivers 105. The training signal is generated by the coding andmodulation subsystem 102, converted to analog by a D/A converter (notshown), and then converted from baseband to RF by each transmitter 103,in succession. Each receive antenna 105 coupled to its RF Receiver 106receives each training signal and converts it to baseband. The basebandsignal is converted to digital by a D/A converter (not shown), and thesignal processing subsystem 107 characterizes the training signal. Eachsignal's characterization may include many factors including, forexample, phase and amplitude relative to a reference internal to thereceiver, an absolute reference, a relative reference, characteristicnoise, or other factors. Each signal's characterization is typicallydefined as a vector that characterizes phase and amplitude changes ofseveral aspects of the signal when it is transmitted across the channel.For example, in a quadrature amplitude modulation (“QAM”)-modulatedsignal the characterization might be a vector of the phase and amplitudeoffsets of several multipath images of the signal. As another example,in an orthogonal frequency division multiplexing (“OFDM”)-modulatedsignal, it might be a vector of the phase and amplitude offsets ofseveral or all of the individual sub-signals in the OFDM spectrum.

The signal processing subsystem 107 stores the channel characterizationreceived by each receiving antenna 105 and corresponding receiver 106.After all three transmit antennas 104 have completed their trainingsignal transmissions, then the signal processing subsystem 107 will havestored three channel characterizations for each of three receivingantennas 105, resulting in a 3×3 matrix 108, designated as the channelcharacterization matrix, “H.” Each individual matrix element is thechannel characterization (which is typically a vector, as describedabove) of the training signal transmission of transmit antenna 104 i asreceived by the receive antenna 105 j.

At this point, the signal processing subsystem 107 inverts the matrix H108, to produce H⁻¹, and awaits transmission of actual data fromtransmit antennas 104. Note that various prior art MIMO techniquesdescribed in available literature, can be utilized to ensure that the Hmatrix 108 can be inverted.

In operation, a payload of data to be transmitted is presented to thedata Input subsystem 100. It is then divided up into three parts bysplitter 101 prior to being presented to coding and modulation subsystem102. For example, if the payload is the ASCII bits for “abcdef,” itmight be divided up into three sub-payloads of ASCII bits for “ad,”“be,” and “cf” by Splitter 101. Then, each of these sub-payloads ispresented individually to the coding and modulation subsystem 102.

Each of the sub-payloads is individually coded by using a coding systemsuitable for both statistical independence of each signal and errorcorrection capability. These include, but are not limited toReed-Solomon coding, Viterbi coding, and Turbo Codes. Finally, each ofthe three coded sub-payloads is modulated using an appropriatemodulation scheme for the channel. Examples of modulation schemes aredifferential phase shift key (“DPSK”) modulation, 64-QAM modulation andOFDM. It should be noted here that the diversity gains provided by MIMOallow for higher-order modulation constellations that would otherwise befeasible in a SISO (Single Input-Single Output) system utilizing thesame channel. Each coded and modulated signal is then transmittedthrough its own antenna 104 following D/A conversion by a D/A conversionunit (not shown) and RF generation by each transmitter 103.

Assuming that adequate spatial diversity exists amongst the transmit andreceive antennas, each of the receiving antennas 105 will receive adifferent combination of the three transmitted signals from antennas104. Each signal is received and converted down to baseband by each RFreceiver 106, and digitized by an A/D converter (not shown). If y_(n) isthe signal received by the nth receive antenna 105, and x_(n) is thesignal transmitted by nth transmit antenna 104, and N is noise, this canbe described by the following three equations:y ₁ =x ₁ H ₁₁ +x ₂ H ₁₂ +x ₃ H ₁₃ +Ny ₂ =x ₁ H ₂₁ +x ₂ H ₂₂ +x ₃ H ₂₃ +Ny ₃ =x ₁ H ₃₁ +x ₂ H ₃₂ +x ₃ H ₃₃ +N

Given that this is a system of three equations with three unknowns, itis a matter of linear algebra for the signal processing subsystem 107 toderive x₁, x₂, and x₃ (assuming that N is at a low enough level topermit decoding of the signals):x ₁ =y ₁ H ⁻¹ ₁₁ +y ₂ H ⁻¹ ₁₂ +y ₃ H ⁻¹ ₁₃x ₂ =y ₁ H ⁻¹ ₂₁ +y ₂ H ⁻¹ ₂₂ +y ₃ H ⁻¹ ₂₃x ₃ =y ₁ H ⁻¹ ₃₁ +y ₂ H ⁻¹ ₃₂ +y ₃ H ⁻¹ ₃₃

Once the three transmitted signals x_(n) are thus derived, they are thendemodulated, decoded, and error-corrected by signal processing subsystem107 to recover the three bit streams that were originally separated outby splitter 101. These bit streams are combined in combiner unit 108,and output as a single data stream from the data output 109. Assumingthe robustness of the system is able to overcome the noise impairments,the data output 109 will produce the same bit stream that was introducedto the data Input 100.

Although the prior art system just described is generally practical upto four antennas, and perhaps up to as many as 10, for the reasonsdescribed in the Background section of this disclosure, it becomesimpractical with large numbers of antennas (e.g. 25, 100, or 1000).

Typically, such a prior art system is two-way, and the return path isimplemented exactly the same way, but in reverse, with each side of thecommunications channels having both transmit and receive subsystems.

FIG. 2 illustrates one embodiment of the invention in which a BaseStation (BS) 200 is configured with a Wide Area Network (WAN) interface(e.g. to the Internet through a T1 or other high speed connection) 201and is provisioned with a number (N) of antennas 202. For the timebeing, we use the term “Base Station” to refer to any wireless stationthat communicates wirelessly with a set of clients from a fixedlocation. Examples of Base Stations are access points in wireless localarea networks (WLANs) or WAN antenna tower or antenna array. There are anumber of Client Devices 203-207, each with a single antenna, which areserved wirelessly from the Base Station 200. Although for the purposesof this example it is easiest to think about such a Base Station asbeing located in an office environment where it is serving ClientDevices 203-207 that are wireless-network equipped personal computers,this architecture will apply to a large number of applications, bothindoor and outdoor, where a Base Station is serving wireless clients.For example, the Base Station could be based at a cellular phone tower,or on a television broadcast tower. In one embodiment, the Base Station200 is positioned on the ground and is configured to transmit upward atHF frequencies (e.g., frequencies up to 24 MHz) to bounce signals offthe ionosphere as described in co-pending application entitled SYSTEMAND METHOD FOR ENHANCING NEAR VERTICAL INCIDENCE SKYWAVE (“NVIS”)COMMUNICATION USING SPACE-TIME CODING, Ser. No. 10/817,731, Filed Apr.2, 2004, which is assigned to the assignee of the present applicationand which is incorporated herein by reference.

Certain details associated with the Base Station 200 and Client Devices203-207 set forth above are for the purpose of illustration only and arenot required for complying with the underlying principles of theinvention. For example, the Base Station may be connected to a varietyof different types of wide area networks via WAN interface 201 includingapplication-specific wide area networks such as those used for digitalvideo distribution. Similarly, the Client Devices may be any variety ofwireless data processing and/or communication devices including, but notlimited to cellular phones, personal digital assistants (“PDAs”),receivers, and wireless cameras.

In one embodiment, the Base Station's n Antennas 202 are separatedspatially such that each is transmitting and receiving signals which arenot spatially correlated, just as if the Base Station was a prior artMIMO transceiver. As described in the Background, experiments have beendone where antennas placed within λ/6 (i.e. ⅙ wavelength) apartsuccessfully achieve an increase in throughput from MIMO, but generallyspeaking, the further apart these Base Station antennas are placed, thebetter the system performance, and λ/2 is a desirable minimum. Ofcourse, the underlying principles of the invention are not limited toany particular separation between antennas.

Note that a single Base Station 200 may very well have its antennaslocated very far apart. For example, in the HF spectrum, the antennasmay be 10 meters apart or more (e.g., in an NVIS implementationmentioned above). If 100 such antennas are used, the Base Station'santenna array could well occupy several square kilometers.

In addition to spatial diversity techniques, one embodiment of theinvention polarizes the signal in order to increase the effectivethroughput of the system. Increasing channel capacity throughpolarization is a well known technique which has been employed bysatellite television providers for years. Using polarization, it ispossible to have multiple (e.g., three) Base Station or users' antennasvery close to each other, and still be not spatially correlated.Although conventional RF systems usually will only benefit from thediversity of two dimensions (e.g. x and y) of polarization, thearchitecture described herein may further benefit from the diversity ofthree dimensions of polarization (x, y and z).

In addition to space and polarization diversity, one embodiment of theinvention employs antennas with near-orthogonal radiation patterns toimprove link performance via pattern diversity. Pattern diversity canimprove the capacity and error-rate performance of MIMO systems and itsbenefits over other antenna diversity techniques have been shown in thefollowing papers:

-   [13] L. Dong, H. Ling, and R. W. Heath Jr., “Multiple-input    multiple-output wireless communication systems using antenna pattern    diversity,” Proc. IEEE Glob. Telecom. Conf., vol. 1, pp. 997-1001,    November 2002.-   [14] R. Vaughan, “Switched parasitic elements for antenna    diversity,” IEEE Trans. Antennas Propagat., vol. 47, pp. 399-405,    February 1999.-   [15] P. Mattheijssen, M. H. A. J. Herben, G. Dolmans, and L. Leyten,    “Antenna-pattern diversity versus space diversity for use at    handhelds,” IEEE Trans. on Veh. Technol., vol. 53, pp. 1035-1042,    July 2004.-   [16] C. B. Dietrich Jr, K. Dietze, J. R. Nealy, and W. L. Stutzman,    “Spatial, polarization, and pattern diversity for wireless handheld    terminals,” Proc. IEEE Antennas and Prop. Symp., vol. 49, pp.    1271-1281, September 2001.-   [17] A. Forenza and R. W. Heath, Jr., “Benefit of Pattern Diversity    Via 2-element Array of Circular Patch Antennas in Indoor Clustered    MIMO Channels”, IEEE Trans. on Communications, vol. 54, no. 5, pp.    943-954, May 2006.

Using pattern diversity, it is possible to have multiple Base Station orusers' antennas very close to each other, and still be not spatiallycorrelated.

FIG. 3 provides additional detail of one embodiment of the Base Station200 and Client Devices 203-207 shown in FIG. 2. For the purposes ofsimplicity, the Base Station 300 is shown with only three antennas 305and only three Client Devices 306-308. It will be noted, however, thatthe embodiments of the invention described herein may be implementedwith a virtually unlimited number of antennas 305 (i.e., limited only byavailable space and noise) and Client Devices 306-308.

FIG. 3 is similar to the prior art MIMO architecture shown in FIG. 1 inthat both have three antennas on each sides of a communication channel.A notable difference is that in the prior art MIMO system the threeantennas 105 on the right side of FIG. 1 are all a fixed distance fromone another (e.g., integrated on a single device), and the receivedsignals from each of the antennas 105 are processed together in theSignal Processing subsystem 107. By contrast, in FIG. 3, the threeantennas 309 on the right side of the diagram are each coupled to adifferent Client Device 306-308, each of which may be distributedanywhere within range of the Base Station 305. As such, the signal thateach Client Device receives is processed independently from the othertwo received signals in its Coding, Modulation, Signal Processingsubsystem 311. Thus, in contrast to a Multiple-Input (i.e. antennas 105)Multiple-Output (i.e. antennas 104) “MIMO” system, FIG. 3 illustrates aMultiple Input (i.e. antennas 305) Distributed Output (i.e. antennas305) system, referred to hereinafter as a “MIDO” system.

Note that this application uses different terminology than previousapplications, so as to better conform with academic and industrypractices. In previously cited co-pending application, SYSTEM AND METHODFOR ENHANCING NEAR VERTICAL INCIDENCE SKYWAVE (“NVIS”) COMMUNICATIONUSING SPACE-TIME CODING, Ser. No. 10/817,731, Filed Apr. 2, 2004, andapplication Ser. No. 10/902,978 filed Jul. 30, 2004 for which this isapplication is a continuation-in-part, the meaning of “Input” and“Output” (in the context of SIMO, MISO, DIMO and MIDO) is reversed fromhow the terms are used in this application. In the prior applications,“Input” referred to the wireless signals as they are input to thereceiving antennas (e.g. antennas 309 in FIG. 3), and “Output” referredto the wireless signals as they are output by the transmitting antennas(e.g. antennas 305). In academia and the wireless industry, the reversemeaning of “Input” and “Output” is commonly used, in which “Input”refers to the wireless signals as they are input to the channel (i.e.the transmitted wireless signals from antennas 305) and “Output” refersto the wireless signals as they are output from the channel (i.e.wireless signals received by antennas 309). This application adopts thisterminology, which is the reverse of the applications cited previouslyin this paragraph. Thus, the following terminology equivalences shall bedrawn between applications:

10/817,731 and Current 10/902,978 Application SIMO = MISO MISO = SIMODIMO = MIDO MIDO = DIMO

The MIDO architecture shown in FIG. 3 achieves a similar capacityincrease as MIMO over a SISO system for a given number of transmittingantennas. However, one difference between MIMO and the particular MIDOembodiment illustrated in FIG. 3 is that, to achieve the capacityincrease provided by multiple base station antennas, each MIDO ClientDevice 306-308 requires only a single receiving antenna, whereas withMIMO, each Client Device requires as least as many receiving antennas asthe capacity multiple that is hoped to be achieved. Given that there isusually a practical limit to how many antennas can be placed on a ClientDevice (as explained in the Background), this typically limits MIMOsystems to between four to ten antennas (and 4× to 10× capacitymultiple). Since the Base Station 300 is typically serving many ClientDevices from a fixed and powered location, is it practical to expand itto far more antennas than ten, and to separate the antennas by asuitable distance to achieve spatial diversity. As illustrated, eachantenna is equipped with a transceiver 304 and a portion of theprocessing power of a Coding, Modulation, and Signal Processing section303. Significantly, in this embodiment, no matter how much Base Station300 is expanded, each Client Device 306-308 only will require oneantenna 309, so the cost for an individual user Client Device 306-308will be low, and the cost of Base Station 300 can be shared among alarge base of users.

An example of how a MIDO transmission from the Base Station 300 to theClient Devices 306-308 can be accomplished is illustrated in FIGS. 4through 6.

In one embodiment of the invention, before a MIDO transmission begins,the channel is characterized. As with a MIMO system, a training signalis transmitted (in the embodiment herein described), one-by-one, by eachof the antennas 405. FIG. 4 illustrates only the first training signaltransmission, but with three antennas 405 there are three separatetransmissions in total. Each training signal is generated by the Coding,Modulation, and Signal Processing subsystem 403, converted to analogthrough a D/A converter, and transmitted as RF through each RFTransceiver 404. Various different coding, modulation and signalprocessing techniques may be employed including, but not limited to,those described above (e.g., Reed Solomon, Viterbi coding; QAM, DPSK,QPSK modulation, . . . etc).

Each Client Device 406-408 receives a training signal through itsantenna 409 and converts the training signal to baseband by Transceiver410. An A/D converter (not shown) converts the signal to digital whereis it processed by each Coding, Modulation, and Signal Processingsubsystem 411. Signal characterization logic 320 then characterizes theresulting signal (e.g., identifying phase and amplitude distortions asdescribed above) and stores the characterization in memory. Thischaracterization process is similar to that of prior art MIMO systems,with a notable difference being that the each client device onlycomputes the characterization vector for its one antenna, rather thanfor n antennas. For example, the Coding Modulation and Signal Processingsubsystem 420 of client device 406 is initialized with a known patternof the training signal (either at the time of manufacturing, byreceiving it in a transmitted message, or through another initializationprocess). When antenna 405 transmits the training signal with this knownpattern, Coding Modulation and Signal Processing subsystem 420 usescorrelation methods to find the strongest received pattern of thetraining signal, it stores the phase and amplitude offset, then itsubtracts this pattern from the received signal. Next, it finds thensecond strongest received pattern that correlates to the trainingsignal, it stores the phase and amplitude offset, then it subtracts thissecond strongest pattern from the received signal. This processcontinues until either some fixed number of phase and amplitude offsetsare stored (e.g. eight), or a detectable training signal pattern dropsbelow a given noise floor. This vector of phase/amplitude offsetsbecomes element H₁₁ of the vector 413. Simultaneously, Coding Modulationand Signal Processing subsystems for Client Devices 407 and 408implement the produce their vector elements H₂₁ and H₃₁.

The memory in which the characterization is stored may be a non-volatilememory such as a Flash memory or a hard drive and/or a volatile memorysuch as a random access memory (e.g., SDRAM, RDAM). Moreover, differentClient Devices may concurrently employ different types of memories tostore the characterization information (e.g., PDA's may use Flash memorywhereas notebook computers may use a hard drive). The underlyingprinciples of the invention are not limited to any particular type ofstorage mechanism on the various Client Devices or the Base Station.

As mentioned above, depending on the scheme employed, since each ClientDevice 406-408 has only one antenna, each only stores a 1×3 row 413-415of the H matrix. FIG. 4 illustrates the stage after the first trainingsignal transmission where the first column of 1×3 rows 413-415 has beenstored with channel characterization information for the first of thethree Base Station antennas 405. The remaining two columns are storedfollowing the channel characterization of the next two training signaltransmissions from the remaining two base station antennas. Note thatfor the sake of illustration the three training signals are transmittedat separate times. If the three training signal patterns are chosen suchas not to be correlated to one another, they may be transmittedsimultaneously, thereby reducing training time.

As indicated in FIG. 5, after all three pilot transmissions arecomplete, each Client Device 506-508 transmits back to the Base Station500 the 1×3 row 513-515 of matrix H that it has stored. To the sake ofsimplicity, only one Client Device 506 is illustrated transmitting itscharacterization information in FIG. 5. An appropriate modulation scheme(e.g. DPSK, 64QAM, OFDM) for the channel combined with adequate errorcorrection coding (e.g. Reed Solomon, Viterbi, and/or Turbo codes) maybe employed to make sure that the Base Station 500 receives the data inthe rows 513-515 accurately.

Although all three antennas 505 are shown receiving the signal in FIG.5, it is sufficient for a single antenna and transceiver of the BaseStation 500 to receive each 1×3 row 513-515 transmission. However,utilizing many or all of antennas 505 and Transceivers 504 to receiveeach transmission (i.e., utilizing prior art Single-InputMultiple-Output (“SIMO”) processing techniques in the Coding, Modulationand Signal Processing subsystem 503) may yield a better signal-to-noiseratio (“SNR”) than utilizing a single antenna 505 and Transceiver 504under certain conditions.

As the Coding, Modulation and Signal Processing subsystem 503 of BaseStation 500 receives the 1×3 row 513-515, from each Client Device507-508, it stores it in a 3×3H matrix 516. As with the Client Devices,the Base Station may employ various different storage technologiesincluding, but not limited to non-volatile mass storage memories (e.g.,hard drives) and/or volatile memories (e.g., SDRAM) to store the matrix516. FIG. 5 illustrates a stage at which the Base Station 500 hasreceived and stored the 1×3 row 513 from Client Device 509. The 1×3 rows514 and 515 may be transmitted and stored in H matrix 516 as they arereceived from the remaining Client Devices, until the entire H matrix516 is stored.

One embodiment of a MIDO transmission from a Base Station 600 to ClientDevices 606-608 will now be described with reference to FIG. 66. Becauseeach Client Device 606-608 is an independent device, typically eachdevice is receiving a different data transmission. As such, oneembodiment of a Base Station 600 includes a Router 602 communicativelypositioned between the WAN Interface 601 and the Coding, Modulation andSignal Processing subsystem 603 that sources multiple data streams(formatted into bit streams) from the WAN interface 601 and routes themas separate bit streams u₁-u₃ intended for each Client Device 606-608,respectively. Various well known routing techniques may be employed bythe router 602 for this purpose.

The three bit streams, u₁-u₃, shown in FIG. 6 are then routed into theCoding, Modulation and Signal Processing subsystem 603 and coded intostatistically distinct, error correcting streams (e.g. using ReedSolomon, Viterbi, or Turbo Codes) and modulated using an appropriatemodulation scheme for the channel (such as DPSK, 64QAM or OFDM). Inaddition, the embodiment illustrated in FIG. 6 includes signal precodinglogic 630 for uniquely coding the signals transmitted from each of theantennas 605 based on the signal characterization matrix 616. Morespecifically, rather than routing each of the three coded and modulatedbit streams to a separate antenna (as is done in FIG. 1), in oneembodiment, the precoding logic 630 multiplies the three bit streamsu₁-u₃ in FIG. 6 by the inverse of the H matrix 616, producing three newbit streams, u′₁-u′₃. The three precoded bit streams are then convertedto analog by D/A converters (not shown) and transmitted as RF byTransceivers 604 and antennas 605.

Before explaining how the bit streams are received by the Client Devices606-608, the operations performed by the precoding module 630 will bedescribed. Similar to the MIMO example from FIG. 1 above, the coded andmodulated signal for each of the three source bit streams will bedesignated with u_(n). In the embodiment illustrated in FIG. 6, eachu_(i) contains the data from one of the three bit streams routed by theRouter 602, and each such bit stream is intended for one of the threeClient Devices 606-608.

However, unlike the MIMO example of FIG. 1, where each x_(i) istransmitted by each antenna 104, in the embodiment of the inventionillustrated in FIG. 6, each u_(i) is received at each Client Deviceantenna 609 (plus whatever noise N there is in the channel). To achievethis result, the output of each of the three antennas 605 (each of whichwe will designate as v_(i)) is a function of u_(i) and the H matrix thatcharacterizes the channel for each Client Device. In one embodiment,each v_(i) is calculated by the precoding logic 630 within the Coding,Modulation and Signal Processing subsystem 603 by implementing thefollowing formulas:v ₁ =u ₁ H ⁻¹ ₁₁ +u ₂ H ⁻¹ ₁₂ +u ₃ H ⁻¹ ₁₃v ₂ =u ₁ H ⁻¹ ₂₁ +u ₂ H ⁻¹ ₂₂ +u ₃ H ⁻¹ ₂₃v ₃ =u ₁ H ⁻¹ ₃₁ +u ₂ H ⁻¹ ₃₂ +u ₃ H ⁻¹ ₃₃

Thus, unlike MIMO, where each x_(i) is calculated at the receiver afterthe signals have been transformed by the channel, the embodiments of theinvention described herein solve for each v_(i) at the transmitterbefore the signals have been transformed by the channel. Each antenna609 receives u_(i) already separated from the other u_(n-1) bit streamsintended for the other antennas 609. Each Transceiver 610 converts eachreceived signal to baseband, where it is digitized by an A/D converter(now shown), and each Coding, Modulation and Signal Processing subsystem611, demodulates and decodes the x_(i) bit stream intended for it, andsends its bit stream to a Data Interface 612 to be used by the ClientDevice (e.g., by an application on the client device).

The embodiments of the invention described herein may be implementedusing a variety of different coding and modulation schemes. For example,in an OFDM implementation, where the frequency spectrum is separatedinto a plurality of sub-bands, the techniques described herein may beemployed to characterize each individual sub-band. As mentioned above,however, the underlying principles of the invention are not limited toany particular modulation scheme.

If the Client Devices 606-608 are portable data processing devices suchas PDAs, notebook computers, and/or wireless telephones the channelcharacterization may change frequently as the Client Devices may movefrom one location to another. As such, in one embodiment of theinvention, the channel characterization matrix 616 at the Base Stationis continually updated. In one embodiment, the Base Station 600periodically (e.g., every 250 milliseconds) sends out a new trainingsignal to each Client Device, and each Client Device continuallytransmits its channel characterization vector back to the Base Station600 to ensure that the channel characterization remains accurate (e.g.if the environment changes so as to affect the channel or if a ClientDevice moves). In one embodiment, the training signal is interleavedwithin the actual data signal sent to each client device. Typically, thetraining signals are much lower throughput than the data signals, sothis would have little impact on the overall throughput of the system.Accordingly, in this embodiment, the channel characterization matrix 616may be updated continuously as the Base Station actively communicateswith each Client Device, thereby maintaining an accurate channelcharacterization as the Client Devices move from one location to thenext or if the environment changes so as to affect the channel.

One embodiment of the invention illustrated in FIG. 7 employs MIMOtechniques to improve the upstream communication channel (i.e., thechannel from the Client Devices 706-708 to the Base Station 700). Inthis embodiment, the channel from each of the Client Devices iscontinually analyzed and characterized by upstream channelcharacterization logic 741 within the Base Station. More specifically,each of the Client Devices 706-708 transmits a training signal to theBase Station 700 which the channel characterization logic 741 analyzes(e.g., as in a typical MIMO system) to generate an N×M channelcharacterization matrix 741, where N is the number of Client Devices andM is the number of antennas employed by the Base Station. The embodimentillustrated in FIG. 7 employs three antennas 705 at the Base Station andthree Client Devices 706-608, resulting in a 3×3 channelcharacterization matrix 741 stored at the Base Station 700. The MIMOupstream transmission illustrated in FIG. 7 may be used by the ClientDevices both for transmitting data back to the Base Station 700, and fortransmitting channel characterization vectors back to the Base Station700 as illustrated in FIG. 5. But unlike the embodiment illustrated inFIG. 5 in which each Client Device's channel characterization vector istransmitted at a separate time, the method shown in FIG. 7 allows forthe simultaneous transmission of channel characterization vectors frommultiple Client Devices back to the Base Station 700, therebydramatically reducing the channel characterization vectors' impact onreturn channel throughput.

As mentioned above, each signal's characterization may include manyfactors including, for example, phase and amplitude relative to areference internal to the receiver, an absolute reference, a relativereference, characteristic noise, or other factors. For example, in aquadrature amplitude modulation (“QAM”)-modulated signal thecharacterization might be a vector of the phase and amplitude offsets ofseveral multipath images of the signal. As another example, in anorthogonal frequency division multiplexing (“OFDM”)-modulated signal, itmight be a vector of the phase and amplitude offsets of several or allof the individual sub-signals in the OFDM spectrum. The training signalmay be generated by each Client Device's coding and modulation subsystem711, converted to analog by a D/A converter (not shown), and thenconverted from baseband to RF by each Client Device's transmitter 709.In one embodiment, in order to ensure that the training signals aresynchronized, Client Devices only transmit training signals whenrequested by the Base Station (e.g., in a round robin manner). Inaddition, training signals may be interleaved within or transmittedconcurrently with the actual data signal sent from each client device.Thus, even if the Client Devices 706-708 are mobile, the trainingsignals may be continuously transmitted and analyzed by the upstreamchannel characterization logic 741, thereby ensuring that the channelcharacterization matrix 741 remains up-to-date.

The total channel capacity supported by the foregoing embodiments of theinvention may be defined as min (N, M) where M is the number of ClientDevices and N is the number of Base Station antennas. That is, thecapacity is limited by the number of antennas on either the Base Stationside or the Client side. As such, one embodiment of the inventionemploys synchronization techniques to ensure that no more than min (N,M) antennas are transmitting/receiving at a given time.

In a typical scenario, the number of antennas 705 on the Base Station700 will be less than the number of Client Devices 706-708. An exemplaryscenario is illustrated in FIG. 8 which shows five Client Devices804-808 communicating with a base station having three antennas 802. Inthis embodiment, after determining the total number of Client Devices804-808, and collecting the necessary channel characterizationinformation (e.g., as described above), the Base Station 800 chooses afirst group of three clients 810 with which to communicate (threeclients in the example because min (N, M)=3). After communicating withthe first group of clients 810 for a designated period of time, the BaseStation then selects another group of three clients 811 with which tocommunicate. To distribute the communication channel evenly, the BaseStation 800 selects the two Client Devices 807, 808 which were notincluded in the first group. In addition, because an extra antenna isavailable, the Base Station 800 selects an additional client device 806included in the first group. In one embodiment, the Base Station 800cycles between groups of clients in this manner such that each client iseffectively allocated the same amount of throughput over time. Forexample, to allocate throughput evenly, the Base Station maysubsequently select any combination of three Client Devices whichexcludes Client Device 806 (i.e., because Client Device 806 was engagedin communication with the Base Station for the first two cycles).

In one embodiment, in addition to standard data communications, the BaseStation may employ the foregoing techniques to transmit training signalsto each of the Client Devices and receive training signals and signalcharacterization data from each of the Client Devices.

In one embodiment, certain Client Devices or groups of client devicesmay be allocated different levels of throughput. For example, ClientDevices may be prioritized such that relatively higher priority ClientDevices may be guaranteed more communication cycles (i.e., morethroughput) than relatively lower priority client devices. The“priority” of a Client Device may be selected based on a number ofvariables including, for example, the designated level of a user'ssubscription to the wireless service (e.g., user's may be willing to paymore for additional throughput) and/or the type of data beingcommunicated to/from the Client Device (e.g., real-time communicationsuch as telephony audio and video may take priority over non-real timecommunication such as email).

In one embodiment of the Base Station dynamically allocates throughputbased on the Current Load required by each Client Device. For example,if Client Device 804 is streaming live video and the other devices805-808 are performing non-real time functions such as email, then theBase Station 800 may allocate relatively more throughput to this client804. It should be noted, however, that the underlying principles of theinvention are not limited to any particular throughput allocationtechnique.

As illustrated in FIG. 9, two Client Devices 907, 908 may be so close inproximity, that the channel characterization for the clients iseffectively the same. As a result, the Base Station will receive andstore effectively equivalent channel characterization vectors for thetwo Client Devices 907, 908 and therefore will not be able to createunique, spatially distributed signals for each Client Device.Accordingly, in one embodiment, the Base Station will ensure that anytwo or more Client Devices which are in close proximity to one anotherare allocated to different groups. In FIG. 9, for example, the BaseStation 900 first communicates with a first group 910 of Client Devices904, 905 and 908; and then with a second group 911 of Client Devices905, 906, 907, ensuring that Client Devices 907 and 908 are in differentgroups.

Alternatively, in one embodiment, the Base Station 900 communicates withboth Client Devices 907 and 908 concurrently, but multiplexes thecommunication channel using known channel multiplexing techniques. Forexample, the Base Station may employ time division multiplexing (“TDM”),frequency division multiplexing (“FDM”) or code division multiple access(“CDMA”) techniques to divide the single, spatially-correlated signalbetween Client Devices 907 and 908.

Although each Client Device described above is equipped with a singleantenna, the underlying principles of the invention may be employedusing Client Devices with multiple antennas to increase throughput. Forexample, when used on the wireless systems described above, a clientwith 2 antennas will realize a 2× increase in throughput, a client with3 antennas will realize a 3× increase in throughput, and so on (i.e.,assuming that the spatial and angular separation between the antennas issufficient). The Base Station may apply the same general rules whencycling through Client Devices with multiple antennas. For example, itmay treat each antenna as a separate client and allocate throughput tothat “client” as it would any other client (e.g., ensuring that eachclient is provided with an adequate or equivalent period ofcommunication).

As mentioned above, one embodiment of the invention employs the MIDOand/or MIMO signal transmission techniques described above to increasethe signal-to-noise ratio and throughput within a Near VerticalIncidence Skywave (“NVIS”) system. Referring to FIG. 10, in oneembodiment of the invention, a first NVIS station 1001 equipped with amatrix of N antennas 1002 is configured to communicate with M clientdevices 1004. The NVIS antennas 1002 and antennas of the various clientdevices 1004 transmit signals upward to within about 15 degrees ofvertical in order to achieve the desired NVIS and minimize ground waveinterference effects. In one embodiment, the antennas 1002 and clientdevices 1004, support multiple independent data streams 1006 using thevarious MIDO and MIMO techniques described above at a designatedfrequency within the NVIS spectrum (e.g., at a carrier frequency at orbelow 23 MHz, but typically below 10 MHz), thereby significantlyincreasing the throughput at the designated frequency (i.e., by a factorproportional to the number of statistically independent data streams).

The NVIS antennas serving a given station may be physically very farapart from each other. Given the long wavelengths below 10 MHz and thelong distance traveled for the signals (as much as 300 miles roundtrip), physical separation of the antennas by 100s of yards, and evenmiles, can provide advantages in diversity. In such situations, theindividual antenna signals may be brought back to a centralized locationto be processed using conventional wired or wireless communicationssystems. Alternatively, each antenna can have a local facility toprocess its signals, then use conventional wired or wirelesscommunications systems to communicate the data back to a centralizedlocation. In one embodiment of the invention, NVIS Station 1001 has abroadband link 1015 to the Internet 1010 (or other wide area network),thereby providing the client devices 1003 with remote, high speed,wireless network access.

In one embodiment, the Base Station and/or users may exploitpolarization/pattern diversity techniques described above to reduce thearray size and/or users' distance while providing diversity andincreased throughput. As an example, in MIDO systems with HFtransmissions, the users may be in the same location and yet theirsignals be uncorrelated because of polarization/pattern diversity. Inparticular, by using pattern diversity, one user may be communicating tothe Base Station via groundwave whereas the other user via NVIS.

Additional Embodiments of the Invention

DIDO-OFDM Precoding with I/Q Imbalance

One embodiment of the invention employs a system and method tocompensate for in-phase and quadrature (I/Q) imbalance indistributed-input distributed-output (DIDO) systems with orthogonalfrequency division multiplexing (OFDM). Briefly, according to thisembodiment, user devices estimate the channel and feedback thisinformation to the Base Station; the Base Station computes the precodingmatrix to cancel inter-carrier and inter-user interference caused by I/Qimbalance; and parallel data streams are transmitted to multiple userdevices via DIDO precoding; the user devices demodulate data viazero-forcing (ZF), minimum mean-square error (MMSE) or maximumlikelihood (ML) receiver to suppress residual interference.

As described in detail below, some of the significant features of thisembodiment of the invention include, but are not limited to:

Precoding to cancel inter-carrier interference (ICI) from mirror tones(due to I/Q mismatch) in OFDM systems;

Precoding to cancel inter-user interference and ICI (due to I/Qmismatch) in DIDO-OFDM systems;

Techniques to cancel ICI (due to I/Q mismatch) via ZF receiver inDIDO-OFDM systems employing block diagonalization (BD) precoder;

Techniques to cancel inter-user interference and ICI (due to I/Qmismatch) via precoding (at the transmitter) and a ZF or MMSE filter (atthe receiver) in DIDO-OFDM systems;

Techniques to cancel inter-user interference and ICI (due to I/Qmismatch) via pre-coding (at the transmitter) and a nonlinear detectorlike a maximum likelihood (ML) detector (at the receiver) in DIDO-OFDMsystems;

The use of pre-coding based on channel state information to cancelinter-carrier interference (ICI) from mirror tones (due to I/Q mismatch)in OFDM systems;

The use of pre-coding based on channel state information to cancelinter-carrier interference (ICI) from mirror tones (due to I/Q mismatch)in DIDO-OFDM systems;

The use of an I/Q mismatch aware DIDO precoder at the station and anIQ-aware DIDO receiver at the user terminal;

The use of an I/Q mismatch aware DIDO precoder at the station, an I/Qaware DIDO receiver at the user terminal, and an I/Q aware channelestimator;

The use of an I/Q mismatch aware DIDO precoder at the station, an I/Qaware DIDO receiver at the user terminal, an I/Q aware channelestimator, and an I/Q aware DIDO feedback generator that sends channelstate information from the user terminal to the station;

The use of an I/Q mismatch-aware DIDO precoder at the station and an I/Qaware DIDO configurator that uses I/Q channel information to performfunctions including user selection, adaptive coding and modulation,space-time-frequency mapping, or precoder selection;

The use of an I/Q aware DIDO receiver that cancels ICI (due to I/Qmismatch) via ZF receiver in DIDO-OFDM systems employing blockdiagonalization (BD) precoder;

The use of an I/Q aware DIDO receiver that cancels ICI (due to I/Qmismatch) via pre-coding (at the transmitter) and a nonlinear detectorlike a maximum likelihood detector (at the receiver) in DIDO-OFDMsystems; and

The use of an I/Q aware DIDO receiver that cancels ICI (due to I/Qmismatch) via ZF or MMSE filter in DIDO-OFDM systems.

a. Background

The transmit and receive signals of typical wireless communicationsystems consist of in-phase and quadrature (I/Q) components. Inpractical systems, the inphase and quadrature components may bedistorted due to imperfections in the mixing and baseband operations.These distortions manifest as I/Q phase, gain and delay mismatch. Phaseimbalance is caused by the sine and cosine in the modulator/demodulatornot being perfectly orthogonal. Gain imbalance is caused by differentamplifications between the inphase and quadrature components. There maybe an additional distortion, called delay imbalance, due to differencein delays between the I- and Q-rails in the analog circuitry.

In orthogonal frequency division multiplexing (OFDM) systems, I/Qimbalance causes inter-carrier interference (ICI) from the mirror tones.This effect has been studied in the literature and methods to compensatefor I/Q mismatch in single-input single-output SISO-OFDM systems havebeen proposed in M. D. Benedetto and P. Mandarini, “Analysis of theeffect of the I/Q baseband filter mismatch in an OFDM modem,” Wirelesspersonal communications, pp. 175-186, 2000; S. Schuchert and R.Hasholzner, “A novel I/Q imbalance compensation scheme for the receptionof OFDM signals,” IEEE Transaction on Consumer Electronics, August 2001;M. Valkama, M. Renfors, and V. Koivunen, “Advanced methods for I/Qimbalance compensation in communication receivers,” IEEE Trans. Sig.Proc., October 2001; R. Rao and B. Daneshrad, “Analysis of I/Q mismatchand a cancellation scheme for OFDM systems,” IST Mobile CommunicationSummit, June 2004; A. Tarighat, R. Bagheri, and A. H. Sayed,“Compensation schemes and performance analysis of IQ imbalances in OFDMreceivers,” Signal Processing, IEEE Transactions on [see also Acoustics,Speech, and Signal Processing, IEEE Transactions on], vol. 53, pp.3257-3268, August 2005.

An extension of this work to multiple-input multiple-output MIMO-OFDMsystems was presented in R. Rao and B. Daneshrad, “I/Q mismatchcancellation for MIMO OFDM systems,” in Personal, Indoor and MobileRadio Communications, 2004; PIMRC 2004. 15th IEEE InternationalSymposium on, vol. 4, 2004, pp. 2710-2714. R. M. Rao, W. Zhu, S. Lang,C. Oberli, D. Browne, J. Bhatia, J. F. Frigon, J. Wang, P; Gupta, H.Lee, D. N. Liu, S. G. Wong, M. Fitz, B. Daneshrad, and O. Takeshita,“Multi-antenna testbeds for research and education in wirelesscommunications,” IEEE Communications Magazine, vol. 42, no. 12, pp.72-81, December 2004; S. Lang, M. R. Rao, and B. Daneshrad, “Design anddevelopment of a 5.25 GHz software defined wireless OFDM communicationplatform,” IEEE Communications Magazine, vol. 42, no. 6, pp. 6-12, June2004, for spatial multiplexing (SM) and in A. Tarighat and A. H. Sayed,“MIMO OFDM receivers for systems with IQ imbalances,” IEEE Trans. Sig.Proc., vol. 53, pp. 3583-3596, September 2005, for orthogonal space-timeblock codes (OSTBC).

Unfortunately, there is currently no literature on how to correct forI/Q gain and phase imbalance errors in a distributed-inputdistributed-output (DIDO) communication system. The embodiments of theinvention described below provide a solution to these problems.

DIDO systems consist of one Base Station with distributed antennas thattransmits parallel data streams (via pre-coding) to multiple users toenhance downlink throughput, while exploiting the same wirelessresources (i.e., same slot duration and frequency band) as conventionalSISO systems. A detailed description of DIDO systems was presented in S.G. Perlman and T. Cotter, “System and Method for DistributedInput-Distributed Output Wireless Communications,” Ser. No. 10/902,978,filed Jul. 30, 2004 (“Prior Application”), which is assigned to theassignee of the present application and which is incorporated herein byreference.

There are many ways to implement DIDO precoders. One solution is blockdiagonalization (BD) described in Q. H. Spencer, A. L. Swindlehurst, andM. Haardt, “Zero forcing methods for downlink spatial multiplexing inmultiuser MIMO channels,” IEEE Trans. Sig. Proc., vol. 52, pp. 461-471,February 2004. K. K. Wong, R. D. Murch, and K. B. Letaief, “A jointchannel diagonalization for multiuser MIMO antenna systems,” IEEE Trans.Wireless Comm., vol. 2, pp. 773-786, July 2003; L. U. Choi and R. D.Murch, “A transmit preprocessing technique for multiuser MIMO systemsusing a decomposition approach,” IEEE Trans. Wireless Comm., vol. 3, pp.20-24, January 2004; Z. Shen, J. G. Andrews, R. W. Heath, and B. L.Evans, “Low complexity user selection algorithms for multiuser MIMOsystems with block diagonalization,” accepted for publication in IEEETrans. Sig. Proc., September 2005; Z. Shen, R. Chen, J. G. Andrews, R.W. Heath, and B. L. Evans, “Sum capacity of multiuser MIMO broadcastchannels with block diagonalization,” submitted to IEEE Trans. WirelessComm., October 2005; R. Chen, R. W. Heath, and J. G. Andrews, “Transmitselection diversity for unitary precoded multiuser spatial multiplexingsystems with linear receivers,” accepted to IEEE Trans. on SignalProcessing, 2005. The methods for I/Q compensation presented in thisdocument assume BD precoder, but can be extended to any type of DIDOprecoder.

In DIDO-OFDM systems, I/Q mismatch causes two effects: ICI andinter-user interference. The former is due to interference from themirror tones as in SISO-OFDM systems. The latter is due to the fact thatI/Q mismatch destroys the orthogonality of the DIDO precoder yieldinginterference across users. Both of these types of interference can becancelled at the transmitter and receiver through the methods describedherein. Three methods for I/Q compensation in DIDO-OFDM systems aredescribed and their performance is compared against systems with andwithout I/Q mismatch. Results are presented based both on simulationsand practical measurements carried out with the DIDO-OFDM prototype.

The present embodiments are an extension of the Prior Application. Inparticular, these embodiments relate to the following features of thePrior Application:

The system as described in the prior application, where the I/Q railsare affected by gain and phase imbalance;

The training signals employed for channel estimation are used tocalculate the DIDO precoder with I/Q compensation at the transmitter;and

The signal characterization data accounts for distortion due to I/Qimbalance and is used at the transmitter to compute the DIDO precoderaccording to the method proposed in this document.

b. Embodiments of the Invention

First, the mathematical model and framework of the invention will bedescribed.

Before presenting the solution, it is useful to explain the coremathematical concept. We explain it assuming I/Q gain and phaseimbalance (phase delay is not included in the description but is dealtwith automatically in the DIDO-OFDM version of the algorithm). Toexplain the basic idea, suppose that we want to multiply two complexnumbers s=s_(I)+js_(Q) and h=h_(I)+jh_(Q) and let x=h*s. We use thesubscripts to denote inphase and quadrature components. Recall thatx _(I) =s _(I) h _(I) −s _(Q) h _(Q)andx _(Q) =s _(I) h _(Q) −s _(Q) h _(I)

In matrix form this can be rewritten as

$\begin{bmatrix}x_{I} \\x_{Q}\end{bmatrix} = {{\begin{bmatrix}h_{I} & {- h_{Q}} \\h_{Q} & h_{I}\end{bmatrix}\begin{bmatrix}s_{I} \\s_{Q}\end{bmatrix}}.}$

Note the unitary transformation by the channel matrix (H). Now supposethat s is the transmitted symbol and h is the channel. The presence ofI/Q gain and phase imbalance can be modeled by creating a non-unitarytransformation as follows

$\begin{matrix}{\begin{bmatrix}x_{I} \\x_{Q}\end{bmatrix} = {{\begin{bmatrix}h_{11} & h_{12} \\h_{21} & h_{22}\end{bmatrix}\begin{bmatrix}s_{I} \\s_{Q}\end{bmatrix}}.}} & (A)\end{matrix}$

The trick is to recognize that it is possible to write

$\begin{bmatrix}h_{11} & h_{12} \\h_{21} & h_{22}\end{bmatrix} = {{\frac{1}{2}\begin{bmatrix}{h_{11} + h_{22}} & {h_{12} - h_{21}} \\{- \left( {h_{12} - h_{21}} \right)} & {h_{11} + h_{22}}\end{bmatrix}} + {\frac{1}{2}\begin{bmatrix}{h_{11} - h_{22}} & {h_{12} + h_{21}} \\{h_{12} - h_{21}} & {h_{22} - h_{11}}\end{bmatrix}} - {\frac{1}{2}\begin{bmatrix}{h_{11} + h_{22}} & {h_{12} - h_{21}} \\{- \left( {h_{12} - h_{21}} \right)} & {h_{11} + h_{22}}\end{bmatrix}} + {{{\frac{1}{2}\begin{bmatrix}{h_{11} - h_{22}} & {- \left( {h_{12} + h_{21}} \right)} \\{h_{12} + h_{21}} & {h_{11} - h_{22}}\end{bmatrix}}\begin{bmatrix}1 & 0 \\0 & {- 1}\end{bmatrix}}.}}$

Now, rewriting (A)

$\begin{matrix}\begin{matrix}{\begin{bmatrix}x_{I} \\x_{Q}\end{bmatrix} = {{{\frac{1}{2}\begin{bmatrix}{h_{11} + h_{22}} & {h_{12} - h_{21}} \\{- \left( {h_{12} - h_{21}} \right)} & {h_{11} + h_{22}}\end{bmatrix}}\begin{bmatrix}s_{I} \\s_{Q}\end{bmatrix}} +}} \\{{{\frac{1}{2}\begin{bmatrix}{h_{11} - h_{22}} & {- \left( {h_{12} + h_{21}} \right)} \\{h_{12} + h_{21}} & {h_{11} - h_{22}}\end{bmatrix}}\begin{bmatrix}1 & 0 \\0 & {- 1}\end{bmatrix}}\begin{bmatrix}s_{I} \\s_{Q}\end{bmatrix}} \\{= {{{\frac{1}{2}\begin{bmatrix}{h_{11} + h_{22}} & {h_{12} - h_{21}} \\{- \left( {h_{12} - h_{21}} \right)} & {h_{11} + h_{22}}\end{bmatrix}}\begin{bmatrix}s_{I} \\s_{Q}\end{bmatrix}} +}} \\{{\frac{1}{2}\begin{bmatrix}{h_{11} - h_{22}} & {- \left( {h_{12} + h_{21}} \right)} \\{h_{12} + h_{21}} & {h_{11} - h_{22}}\end{bmatrix}}\begin{bmatrix}s_{I} \\{- s_{Q}}\end{bmatrix}}\end{matrix} & (5)\end{matrix}$

Let us define

$\mathcal{H}_{e} = {{\frac{1}{2}\begin{bmatrix}{h_{11} + h_{22}} & {h_{12} - h_{21}} \\{- \left( {h_{12} - h_{21}} \right)} & {h_{11} + h_{22}}\end{bmatrix}}\mspace{14mu}{and}}$$\mathcal{H}_{c} = {{\frac{1}{2}\begin{bmatrix}{h_{11} - h_{22}} & {- \left( {h_{12} + h_{21}} \right)} \\{h_{12} + h_{21}} & {h_{11} - h_{22}}\end{bmatrix}}.}$

Both of these matrices have a unitary structure thus can be equivalentlyrepresented by complex scalars ash _(e) =h ₁₁ +h ₂₂ +j(h ₂₁ −h ₁₂)andh _(c) =h ₁₁ −h ₂₂ +j(h ₂₁ −h ₁₂).

Using all of these observations, we can put the effective equation backin a scalar form with two channels: the equivalent channel h_(e) and theconjugate channel h_(c). Then the effective transformation in (5)becomesx=h _(e) s+h _(c) s*.

We refer to the first channel as the equivalent channel and the secondchannel as the conjugate channel. The equivalent channel is the one youwould observe if there were no I/Q gain and phase imbalance.

Using similar arguments, it can be shown that the input-outputrelationship of a discrete-time MIMO N×M system with I/Q gain and phaseimbalance is (using the scalar equivalents to build their matrixcounterparts)

${x\lbrack t\rbrack} = {{\sum\limits_{l = 0}^{L}{{h_{e}\lbrack l\rbrack}{s\left\lbrack {t - l} \right\rbrack}}} + {{h_{c}\lbrack l\rbrack}{s^{*}\left\lbrack {t - l} \right\rbrack}}}$where t is the discrete time index, h_(e), h_(c)∈C^(M×N), s=[s₁, . . . ,s_(N)], x=[x₁, . . . , x_(M)] and L is the number of channel taps.

In DIDO-OFDM systems, the received signal in the frequency domain isrepresented. Recall from signals and systems that ifFFT _(K) {s[t]}=S[k] then FFT _(K) {s*[t]}=S*[(−k)]=S*[K−k] for k=0,1, .. . ,K−1.With OFDM, the equivalent input-output relationship for a MIMO-OFDMsystem for subcarrier k isx[k]=H _(e) [k]s[k]+H _(c) [k]s*[K−k]  (1)where k=0, 1, . . . , K−1 is the OFDM subcarrier index, H_(e) and H_(c)denote the equivalent and conjugate channel matrices, respectively,defined as

${H_{e}\lbrack k\rbrack} = {\sum\limits_{l = 0}^{L}{{h_{e}\lbrack l\rbrack}e^{{- j}\;\frac{2{\prod\; k}}{K}l}\mspace{14mu}{and}}}$${H_{c}\lbrack k\rbrack} = {\sum\limits_{l = 0}^{L}{{h_{c}\lbrack l\rbrack}{e^{{- j}\;\frac{2{\prod\; k}}{K}l}.}}}$

The second contribution in (1) is interference from the mirror tone. Itcan be dealt with by constructing the following stacked matrix system(note carefully the conjugates)

$\begin{bmatrix}{\overset{\_}{x}\lbrack k\rbrack} \\{{\overset{\_}{x}}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix} = {\begin{bmatrix}{H_{e}\lbrack k\rbrack} & {H_{c}\lbrack k\rbrack} \\{H_{c}^{*}\left\lbrack {K - k} \right\rbrack} & {H_{e}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}\begin{bmatrix}{\overset{\_}{s}\lbrack k\rbrack} \\{{\overset{\_}{s}}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}}$where s=[s ₁,s ₂]^(T) and x=[x ₁,x ₂]^(T) are the vectors of transmitand receive symbols in the frequency domain, respectively.

Using this approach, an effective matrix is built to use for DIDOoperation. For example, with DIDO 2×2 the input-output relationship(assuming each user has a single receive antenna) the first user devicesees (in the absence of noise)

$\begin{matrix}{\begin{bmatrix}{{\overset{\_}{x}}_{1}\lbrack k\rbrack} \\{{\overset{\_}{x}}_{1}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix} = {\begin{bmatrix}{H_{e}^{(1)}\lbrack k\rbrack} & {H_{c}^{(1)}\lbrack k\rbrack} \\{H_{c}^{{(1)}*}\left\lbrack {K - k} \right\rbrack} & {H_{e}^{{(1)}*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}{W\begin{bmatrix}{{\overset{\_}{s}}_{1}\lbrack k\rbrack} \\{{\overset{\_}{s}}_{1}^{*}\left\lbrack {K - k} \right\rbrack} \\{{\overset{\_}{s}}_{2}\lbrack k\rbrack} \\{{\overset{\_}{s}}_{2}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}}}} & (2)\end{matrix}$while the second user observes

$\begin{matrix}{\begin{bmatrix}{{\overset{\_}{x}}_{2}\lbrack k\rbrack} \\{{\overset{\_}{x}}_{2}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix} = {\begin{bmatrix}{H_{e}^{(2)}\lbrack k\rbrack} & {H_{c}^{(2)}\lbrack k\rbrack} \\{H_{c}^{{(2)}*}\left\lbrack {K - k} \right\rbrack} & {H_{e}^{{(2)}*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}{W\begin{bmatrix}{{\overset{\_}{s}}_{1}\lbrack k\rbrack} \\{{\overset{\_}{s}}_{1}^{*}\left\lbrack {K - k} \right\rbrack} \\{{\overset{\_}{s}}_{2}\lbrack k\rbrack} \\{{\overset{\_}{s}}_{2}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}}}} & (3)\end{matrix}$where H_(e) ^((m)), H_(c) ^((m))∈C^(1×2) denote the m-th row of thematrices H_(e) and H_(c), respectively, and W∈C^(4×4) is the DIDOpre-coding matrix. From (2) and (3) it is observed that the receivedsymbol x _(m)[k] of user m is affected by two sources of interferencecaused by I/Q imbalance: inter-carrier interference from the mirror tone(i.e., s*_(m)[K−k]) and inter-user interference (i.e., s _(p)[k] ands*_(p)[K−k] with p≠m). The DIDO precoding matrix W in (3) is designed tocancel these two interference terms.

There are several different embodiments of the DIDO precoder that can beused here depending on joint detection applied at the receiver. In oneembodiment, block diagonalization (BD) is employed (see, e.g., Q. H.Spencer, A. L. Swindlehurst, and M. Haardt, “Zeroforcing methods fordownlink spatial multiplexing in multiuser MIMO channels,” IEEE Trans.Sig. Proc., vol. 52, pp. 461-471, February 2004. K. K. Wong, R. D.Murch, and K. B. Letaief, “A joint channel diagonalization for multiuserMIMO antenna systems,” IEEE Trans. Wireless Comm., vol. 2, pp. 773-786,July 2003. L. U. Choi and R. D. Murch, “A transmit preprocessingtechnique for multiuser MIMO systems using a decomposition approach,”IEEE Trans. Wireless Comm., vol. 3, pp. 20-24, January 2004. Z. Shen, J.G. Andrews, R. W. Heath, and B. L. Evans, “Low complexity user selectionalgorithms for multiuser MIMO systems with block diagonalization,”accepted for publication in IEEE Trans. Sig. Proc., September 2005. Z.Shen, R. Chen, J. G. Andrews, R. W. Heath, and B. L. Evans, “Sumcapacity of multiuser MIMO broadcast channels with blockdiagonalization,” submitted to IEEE Trans. Wireless Comm., October 2005,computed from the composite channel [H_(e) ^((m)), H_(c) ^((m))] (ratherthan H_(e) ^((m))). So, the current DIDO system chooses the precodersuch that

$\begin{matrix}{{H_{w}\overset{\Delta}{=}\begin{bmatrix}{H_{e}^{(1)}\lbrack k\rbrack} & {H_{c}^{(1)}\lbrack k\rbrack} \\{H_{c}^{{(1)}*}\left\lbrack {K - k} \right\rbrack} & {H_{e}^{{(1)}*}\left\lbrack {K - k} \right\rbrack} \\{H_{e}^{(2)}\lbrack k\rbrack} & {H_{c}^{(2)}\lbrack k\rbrack} \\{H_{c}^{{(2)}*}\left\lbrack {K - k} \right\rbrack} & {H_{e}^{{(2)}*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}}\begin{matrix}{W = \begin{bmatrix}\alpha_{1,1} & 0 & 0 & 0 \\0 & \alpha_{1,2} & 0 & 0 \\0 & 0 & \alpha_{2,1} & 0 \\0 & 0 & 0 & \alpha_{2,2}\end{bmatrix}} \\{\overset{\Delta}{=}\begin{bmatrix}H_{w}^{({1,1})} & H_{w}^{({1,2})} \\H_{w}^{({2,1})} & H_{w}^{({2,2})}\end{bmatrix}}\end{matrix}} & (4)\end{matrix}$where α_(i,j) are constants and H_(w) ^((i,j))∈C^(2×2). This method isbeneficial because using this precoder, it is possible to keep otheraspects of the DIDO precoder the same as before, since the effects ofI/Q gain and phase imbalance are completely cancelled at thetransmitter.

It is also possible to design DIDO precoders that pre-cancel inter-userinterference, without pre-cancelling ICI due to 10 imbalance. With thisapproach, the receiver (instead of the transmitter) compensates for theIQ imbalance by employing one of the receive filters described below.Then, the pre-coding design criterion in (4) can be modified as

$\begin{matrix}{{H_{w}\overset{\Delta}{=}\begin{bmatrix}{H_{e}^{(1)}\lbrack k\rbrack} & {H_{c}^{(1)}\lbrack k\rbrack} \\{H_{c}^{{(1)}*}\left\lbrack {K - k} \right\rbrack} & {H_{e}^{{(1)}*}\left\lbrack {K - k} \right\rbrack} \\{H_{e}^{(2)}\lbrack k\rbrack} & {H_{c}^{(2)}\lbrack k\rbrack} \\{H_{c}^{{(2)}*}\left\lbrack {K - k} \right\rbrack} & {H_{e}^{{(2)}*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}}\begin{matrix}{W = \begin{bmatrix}\alpha_{1,1} & \alpha_{1,2} & 0 & 0 \\\alpha_{2,1} & \alpha_{2,2} & 0 & 0 \\0 & 0 & \alpha_{3,3} & \alpha_{3,4} \\0 & 0 & \alpha_{4,3} & \alpha_{4,4}\end{bmatrix}} \\{\overset{\Delta}{=}\begin{bmatrix}H_{w}^{({1,1})} & H_{w}^{({1,2})} \\H_{w}^{({2,1})} & H_{w}^{({2,2})}\end{bmatrix}}\end{matrix}} & (5) \\{{{\overset{\_}{x}}_{1}\lbrack k\rbrack} = \left\lbrack {{\begin{matrix}H_{w}^{({1,1})} & \left. H_{w}^{({1,2})} \right\rbrack\end{matrix}\left\lbrack {\begin{matrix}{\overset{\_}{s}}_{1} \\{\overset{\_}{s}}_{2}\end{matrix}\frac{\lbrack k\rbrack}{\lbrack k\rbrack}} \right\rbrack}\mspace{20mu}{and}} \right.} & (6) \\{{{\overset{\_}{x}}_{2}\lbrack k\rbrack} = \left\lbrack {\begin{matrix}H_{w}^{({2,1})} & \left. H_{w}^{({2,2})} \right\rbrack\end{matrix}\left\lbrack {\begin{matrix}{\overset{\_}{s}}_{1} \\{\overset{\_}{s}}_{2}\end{matrix}\frac{\lbrack k\rbrack}{\lbrack k\rbrack}} \right\rbrack} \right.} & (7)\end{matrix}$

where s _(m)[k]=[s _(m)[k], s*_(m)[K−k]]^(T) for the m-th transmitsymbol and x _(m)[k]=[x _(m)[k], x*_(m)[K−k]]^(T) is the receive symbolvector for user m.

At the receive side, to estimate the transmit symbol vector s _(m)[k],user m employs ZF filter and the estimated symbol vector is given byŝ _(m) ^((ZF)) [k]=[(H _(w) ^((m,m)†) H _(w) ^((m,m)))⁻¹ H _(w)^((m,m)†) ]x _(m) [k]  (8)

While the ZF filter is the easiest to understand, the receiver may applyany number of other filters known to those skilled in the art. Onepopular choice is the MMSE filter whereŝ _(m) ^((MMSE)) [k]=(H _(w) ^((m,m)†) +p ⁻¹ I)⁻¹ H _(w) ^((m,m)) H _(w)^((m,m)†) x _(m) [k]  (9)and ρ is the signal-to-noise ratio. Alternatively, the receiver mayperform a maximum likelihood symbol detection (or sphere decoder oriterative variation). For example, the first user might use the MLreceiver and solve the following optimization

$\begin{matrix}{{{\hat{s}}_{m}^{({ML})}\lbrack k\rbrack} = {\arg\;{\min\limits_{s_{1},{s_{2} \in S}}{{{{\overset{\_}{y}}_{1}\lbrack k\rbrack} - \left\lbrack \begin{matrix}H_{w}^{({1,1})} & {{\left. H_{w}^{({1,2})} \right\rbrack\begin{bmatrix}{s_{1}\lbrack k\rbrack} \\{s_{2}\lbrack k\rbrack}\end{bmatrix}}}\end{matrix} \right.}}}}} & (10)\end{matrix}$where S is the set of all possible vectors s and depends on theconstellation size. The ML receiver gives better performance at theexpense of requiring more complexity at the receiver. A similar set ofequations applies for the second user.

Note that H_(w) ^((1,2)) and H_(w) ^((2,1)) in (6) and (7) are assumedto have zero entries. This assumption holds only if the transmitprecoder is able to cancel completely the inter-user interference as forthe criterion in (4). Similarly, H_(w) ^((1,1)) and H_(w) ^((2,2)) arediagonal matrices only if the transmit precoder is able to cancelcompletely the inter-carrier interference (i.e., from the mirror tones).

FIG. 13 illustrates one embodiment of a framework for DIDO-OFDM systemswith I/Q compensation including IQ-DIDO precoder 1302 within a BaseStation (BS), a transmission channel 1304, channel estimation logic 1306within a user device, and a ZF, MMSE or ML receiver 1308. The channelestimation logic 1306 estimates the channels H_(e) ^((m)) and H_(c)^((m)) via training symbols and feedbacks these estimates to theprecoder 1302 within the AP. The BS computes the DIDO precoder weights(matrix W) to pre-cancel the interference due to I/Q gain and phaseimbalance as well as inter-user interference and transmits the data tothe users through the wireless channel 1304. User device m employs theZF, MMSE or ML receiver 1308, by exploiting the channel estimatesprovided by the unit 1304, to cancel residual interference anddemodulates the data.

The following three embodiments may be employed to implement this I/Qcompensation algorithm:

Method 1—TX compensation: In this embodiment, the transmitter calculatesthe pre-coding matrix according to the criterion in (4). At thereceiver, the user devices employ a “simplified” ZF receiver, whereH_(w) ^((1,1)) and H_(w) ^((2,2)) are assumed to be diagonal matrices.Hence, equation (8) simplifies as

$\begin{matrix}{{{\hat{s}}_{m}\lbrack k\rbrack} = {\begin{bmatrix}{1/\alpha_{m,1}} & 0 \\0 & {1/\alpha_{m,2}}\end{bmatrix}{{{\overset{\_}{x}}_{m}\lbrack k\rbrack}.}}} & (10)\end{matrix}$

Method 2—RX compensation: In this embodiment, the transmitter calculatesthe pre-coding matrix based on the conventional BD method described inR. Chen, R. W. Heath, and J. G. Andrews, “Transmit selection diversityfor unitary precoded multiuser spatial multiplexing systems with linearreceivers,” accepted to IEEE Trans. on Signal Processing, 2005, withoutcanceling inter-carrier and inter-user interference as for the criterionin (4). With this method, the pre-coding matrix in (2) and (3)simplifies as

$\begin{matrix}{W = {\begin{bmatrix}{w_{1,1}\lbrack k\rbrack} & 0 & {w_{1,2}\lbrack k\rbrack} & 0 \\0 & {w_{1,1}^{*}\left\lbrack {K - k} \right\rbrack} & 0 & {w_{1,2}^{*}\left\lbrack {K - k} \right\rbrack} \\{w_{2,1}\lbrack k\rbrack} & 0 & {w_{2,2}\lbrack k\rbrack} & 0 \\0 & {w_{2,1}^{*}\left\lbrack {K - k} \right\rbrack} & 0 & {w_{2,2}^{*}\left\lbrack {K - k} \right\rbrack}\end{bmatrix}.}} & (12)\end{matrix}$

At the receiver, the user devices employ a ZF filter as in (8). Notethat this method does not pre-cancel the interference at the transmitteras in the method 1 above. Hence, it cancels the inter-carrierinterference at the receiver, but it is not able to cancel theinter-user interference. Moreover, in method 2 the users only need tofeedback the vector H_(e) ^((m)) for the transmitter to compute the DIDOprecoder, as opposed to method 1 that requires feedback of both H_(e)^((m)) and H_(c) ^((m)). Therefore, method 2 is particularly suitablefor DIDO systems with low rate feedback channels. On the other hand,method 2 requires slightly higher computational complexity at the userdevice to compute the ZF receiver in (8) rather than (11).

Method 3—TX-RX compensation: In one embodiment, the two methodsdescribed above are combined. The transmitter calculates the pre-codingmatrix as in (4) and the receivers estimate the transmit symbolsaccording to (8).

I/Q imbalance, whether phase imbalance, gain imbalance, or delayimbalance, creates a deleterious degradation in signal quality inwireless communication systems. For this reason, circuit hardware in thepast was designed to have very low imbalance. As described above,however, it is possible to correct this problem using digital signalprocessing in the form of transmit pre-coding and/or a special receiver.One embodiment of the invention comprises a system with several newfunctional units, each of which is important for the implementation ofI/Q correction in an OFDM communication system or a DIDO-OFDMcommunication system.

One embodiment of the invention uses pre-coding based on channel stateinformation to cancel inter-carrier interference (ICI) from mirror tones(due to I/Q mismatch) in an OFDM system. As illustrated in FIG. 11, aDIDO transmitter according to this embodiment includes a user selectorunit 1102, a plurality of coding modulation units 1104, a correspondingplurality of mapping units 1106, a DIDO IQ-aware precoding unit 1108, aplurality of RF transmitter units 1114, a user feedback unit 1112 and aDIDO configurator unit 1110.

The user selector unit 1102 selects data associated with a plurality ofusers U₁-U_(M), based on the feedback information obtained by thefeedback unit 1112, and provides this information each of the pluralityof coding modulation units 1104. Each coding modulation unit 1104encodes and modulates the information bits of each user and send them tothe mapping unit 1106. The mapping unit 1106 maps the input bits tocomplex symbols and sends the results to the DIDO IQ-aware precodingunit 1108. The DIDO IQ-aware precoding unit 1108 exploits the channelstate information obtained by the feedback unit 1112 from the users tocompute the DIDO IQ-aware precoding weights and precoding the inputsymbols obtained from the mapping units 1106. Each of the precoded datastreams is sent by the DIDO IQ-aware precoding unit 1108 to the OFDMunit 1115 that computes the IFFT and adds the cyclic prefix. Thisinformation is sent to the D/A unit 1116 that operates the digital toanalog conversion and send it to the RF unit 1114. The RF unit 1114upconverts the baseband signal to intermediate/radio frequency and sendit to the transmit antenna.

The precoder operates on the regular and mirror tones together for thepurpose of compensating for I/Q imbalance. Any number of precoder designcriteria may be used including ZF, MMSE, or weighted MMSE design. In apreferred embodiment, the precoder completely removes the ICI due to I/Qmismatch thus resulting in the receiver not having to perform anyadditional compensation.

In one embodiment, the precoder uses a block diagonalization criterionto completely cancel inter-user interference while not completelycanceling the I/Q effects for each user, requiring additional receiverprocessing. In another embodiment, the precoder uses a zero-forcingcriterion to completely cancel both inter-user interference and ICI dueto I/Q imbalance. This embodiment can use a conventional DIDO-OFDMprocessor at the receiver.

One embodiment of the invention uses pre-coding based on channel stateinformation to cancel inter-carrier interference (ICI) from mirror tones(due to I/Q mismatch) in a DIDO-OFDM system and each user employs anIQ-aware DIDO receiver. As illustrated in FIG. 12, in one embodiment ofthe invention, a system including the receiver 1202 includes a pluralityof RF units 1208, a corresponding plurality of A/D units 1210, anIQ-aware channel estimator unit 1204 and a DIDO feedback generator unit1206.

The RF units 1208 receive signals transmitted from the DIDO transmitterunits 1,14 downconverts the signals to baseband and provide thedownconverted signals to the A/D units 1210. The A/D units 1210 thenconvert the signal from analog to digital and send it to the OFDM units1213. The OFDM units 1213 remove the cyclic prefix and operates the FFTto report the signal to the frequency domain. During the training periodthe OFDM units 1213 send the output to the IQ-aware channel estimateunit 1204 that computes the channel estimates in the frequency domain.Alternatively, the channel estimates can be computed in the time domain.During the data period the OFDM units 1213 send the output to theIQ-aware receiver unit 1202. The IQ-aware receiver unit 1202 computesthe IQ receiver and demodulates/decodes the signal to obtain the data1214. The IQ-aware channel estimate unit 1204 sends the channelestimates to the DIDO feedback generator unit 1206 that may quantize thechannel estimates and send it back to the transmitter via the feedbackcontrol channel 1112.

The receiver 1202 illustrated in FIG. 12 may operate under any number ofcriteria known to those skilled in the art including ZF, MMSE, maximumlikelihood, or MAP receiver. In one preferred embodiment, the receiveruses an MMSE filter to cancel the ICI caused by IQ imbalance on themirror tones. In another preferred embodiment, the receiver uses anonlinear detector like a maximum likelihood search to jointly detectthe symbols on the mirror tones. This method has improved performance atthe expense of higher complexity.

In one embodiment, an IQ-aware channel estimator 1204 is used todetermine the receiver coefficients to remove ICI. Consequently we claima DIDO-OFDM system that uses pre-coding based on channel stateinformation to cancel inter-carrier interference (ICI) from mirror tones(due to I/Q mismatch), an IQ-aware DIDO receiver, and an IQ-awarechannel estimator. The channel estimator may use a conventional trainingsignal or may use specially constructed training signals sent on theinphase and quadrature signals. Any number of estimation algorithms maybe implemented including least squares, MMSE, or maximum likelihood. TheIQ-aware channel estimator provides an input for the IQ-aware receiver.

Channel state information can be provided to the station through channelreciprocity or through a feedback channel. One embodiment of theinvention comprises a DIDO-OFDM system, with I/Q-aware precoder, with anI/Q-aware feedback channel for conveying channel state information fromthe user terminals to the station. The feedback channel may be aphysical or logical control channel. It may be dedicated or shared, asin a random access channel. The feedback information may be generatedusing a DIDO feedback generator at the user terminal, which we alsoclaim. The DIDO feedback generator takes as an input the output of theI/Q aware channel estimator. It may quantize the channel coefficients ormay use any number of limited feedback algorithms known in the art.

The allocation of users, modulation and coding rate, mapping tospace-time-frequency code slots may change depending on the results ofthe DIDO feedback generator. Thus, one embodiment comprises an IQ-awareDIDO configurator that uses an IQ-aware channel estimate from one ormore users to configure the DIDO IQ-aware precoder, choose themodulation rate, coding rate, subset of users allowed to transmit, andtheir mappings to space-time-frequency code slots.

To evaluate the performance of the proposed compensation methods, threeDIDO 2×2 systems will be compared:

1. With I/Q mismatch: transmit over all the tones (except DC and edgetones), without compensation for I/Q mismatch;

2. With I/Q compensation: transmit over all the tones and compensate forI/Q mismatch by using the “method 1” described above;

3. Ideal: transmit data only over the odd tones to avoid inter-user andinter-carrier (i.e., from the mirror tones) interference caused to I/Qmismatch.

Hereafter, results obtained from measurements with the DIDO-OFDMprototype in real propagation scenarios are presented. FIG. 14 depictsthe 64-QAM constellations obtained from the three systems describedabove. These constellations are obtained with the same users' locationsand fixed average signal-to-noise ratio (˜45 dB). The firstconstellation 1401 is very noisy due to interference from the mirrortones caused by I/Q imbalance. The second constellation 1402 shows someimprovements due to I/Q compensations. Note that the secondconstellation 1402 is not as clean as the ideal case shown asconstellation 1403 due to possible phase noise that yields inter-carrierinterference (ICI).

FIG. 15 shows the average SER (Symbol Error Rate) 1501 and per-usergoodput 1502 performance of DIDO 2×2 systems with 64-QAM and coding rate¾, with and without I/Q mismatch. The OFDM bandwidth is 250 KHz, with 64tones and cyclic prefix length L_(cp)=4. Since in the ideal case wetransmit data only over a subset of tones, SER and goodput performanceis evaluated as a function of the average per-tone transmit power(rather than total transmit power) to guarantee a fair comparison acrossdifferent cases. Moreover, in the following results, we use normalizedvalues of transmit power (expressed in decibel), since our goal here isto compare the relative (rather than absolute) performance of differentschemes. FIG. 15 shows that in presence of I/Q imbalance the SERsaturates, without reaching the target SER (˜10⁻²), consistently to theresults reported in A. Tarighat and A. H. Sayed, “MIMO OFDM receiversfor systems with IQ imbalances,” IEEE Trans. Sig. Proc., vol. 53, pp.3583-3596, September 2005. This saturation effect is due to the factthat both signal and interference (from the mirror tones) power increaseas the TX power increases. Through the proposed I/Q compensation method,however, it is possible to cancel the interference and obtain better SERperformance. Note that the slight increase in SER at high SNR is due toamplitude saturation effects in the DAC, due to the larger transmitpower required for 64-QAM modulations.

Moreover, observe that the SER performance with I/Q compensation is veryclose to the ideal case. The 2 dB gap in TX power between these twocases is due to possible phase noise that yields additional interferencebetween adjacent OFDM tones. Finally, the goodput curves 1502 show thatit is possible to transmit twice as much data when the I/Q method isapplied compared to the ideal case, since we use all the data tonesrather than only the odd tones (as for the ideal case).

FIG. 16 graphs the SER performance of different QAM constellations withand without I/Q compensation. We observe that, in this embodiment, theproposed method is particularly beneficial for 64-QAM constellations.For 4-QAM and 16-QAM the method for I/Q compensation yields worseperformance than the case with I/Q mismatch, possibly because theproposed method requires larger power to enable both data transmissionand interference cancellation from the mirror tones. Moreover, 4-QAM and16-QAM are not as affected by I/Q mismatch as 64-QAM due to the largerminimum distance between constellation points. See A. Tarighat, R.Bagheri, and A. H. Sayed, “Compensation schemes and performance analysisof IQ imbalances in OFDM receivers,” IEEE Transactions on SignalProcessing, vol. 53, pp. 3257-3268, August 2005. This can be alsoobserved in FIG. 16 by comparing the I/Q mismatch against the ideal casefor 4-QAM and 16-QAM. Hence, the additional power required by the DIDOprecoder with interference cancellation (from the mirror tones) does notjustify the small benefit of the I/Q compensation for the cases of 4-QAMand 16-QAM. Note that this issue may be fixed by employing the methods 2and 3 for I/Q compensation described above.

Finally, the relative SER performance of the three methods describedabove is measured in different propagation conditions. For reference,also described is the SER performance in presence of I/Q mismatch. FIG.17 depicts the SER measured for a DIDO 2×2 system with 64-QAM at carrierfrequency of 450.5 MHz and bandwidth of 250 KHz, at two different users'locations. In Location 1 the users are ˜6λ from the BS in differentrooms and NLOS (Non-Line of Sight)) conditions. In Location 2 the usersare ˜λ from the BS in LOS (Line of Sight).

FIG. 17 shows that all three compensation methods always outperform thecase of no compensation. Moreover, it should be noted that method 3outperforms the other two compensation methods in any channel scenario.The relative performance of method 1 and 2 depends on the propagationconditions. It is observed through practical measurement campaigns thatmethod 1 generally outperforms method 2, since it pre-cancels (at thetransmitter) the inter-user interference caused by I/Q imbalance. Whenthis inter-user interference is minimal, method 2 may outperform method1 as illustrated in graph 1702 of FIG. 17, since it does not suffer frompower loss due to the I/Q compensation precoder.

So far, different methods have been compared by considering only alimited set of propagation scenarios as in FIG. 17. Hereafter, therelative performance of these methods in ideal i.i.d. (independent andidentically-distributed) channels is measured. DIDO-OFDM systems aresimulated with I/Q phase and gain imbalance at the transmit and receivesides. FIG. 18 shows the performance of the proposed methods with onlygain imbalance at the transmit side (i.e., with 0.8 gain on the I railof the first transmit chain and gain 1 on the other rails). It isobserved that method 3 outperforms all the other methods. Also, method 1performs better than method 2 in i.i.d. channels, as opposed to theresults obtained in Location 2 in graph 1702 of FIG. 17.

Thus, given the three novel methods to compensate for I/Q imbalance inDIDO-OFDM systems described above, Method 3 outperforms the otherproposed compensation methods. In systems with low rate feedbackchannels, method 2 can be used to reduce the amount of feedback requiredfor the DIDO precoder, at the expense of worse SER performance.

II. Adaptive DIDO Transmission Scheme

Another embodiment of a system and method to enhance the performance ofdistributed-input distributed-output (DIDO) systems will now bedescribed. This method dynamically allocates the wireless resources todifferent user devices, by tracking the changing channel conditions, toincrease throughput while satisfying certain target error rate. The userdevices estimate the channel quality and feedback it to the Base Station(BS); the Base Station processes the channel quality obtained from theuser devices to select the best set of user devices, DIDO scheme,modulation/coding scheme (MCS) and array configuration for the nexttransmission; the Base Station transmits parallel data to multiple userdevices via pre-coding and the signals are demodulated at the receiver.

A system that efficiently allocates resources for a DIDO wireless linkis also described. The system includes a DIDO Base Station with a DIDOconfigurator, which processes feedback received from the users to selectthe best set of users, DIDO scheme, modulation/coding scheme (MCS) andarray configuration for the next transmission; a receiver in a DIDOsystem that measures the channel and other relevant parameters togenerate a DIDO feedback signal; and a DIDO feedback control channel forconveying feedback information from users to the Base Station.

As described in detail below, some of the significant features of thisembodiment of the invention include, but are not limited to:

Techniques to adaptively select number of users, DIDO transmissionschemes (i.e., antenna selection or multiplexing), modulation/codingscheme (MCS) and array configurations based on the channel qualityinformation, to minimize SER or maximize per-user or downlink spectralefficiency;

Techniques to define sets of DIDO transmission modes as combinations ofDIDO schemes and MCSs;

Techniques to assign different DIDO modes to different time slots, OFDMtones and DIDO substreams, depending on the channel conditions;

Techniques to dynamically assign different DIDO modes to different usersbased on their channel quality;

Criteria to enable adaptive DIDO switching based on link quality metricscomputed in the time, frequency and space domains;

Criteria to enable adaptive DIDO switching based on lookup tables.

A DIDO system with a DIDO configurator at the Base Station as in FIG. 19to adaptively select the number of users, DIDO transmission schemes(i.e., antenna selection or multiplexing), modulation/coding scheme(MCS) and array configurations based on the channel quality information,to minimize SER or maximize per user or downlink spectral efficiency;

A DIDO system with a DIDO configurator at the Base Station and a DIDOfeedback generator at each user device as in FIG. 20, which uses theestimated channel state and/or other parameters like the estimated SNRat the receiver to generate a feedback message to be input into the DIDOconfigurator.

A DIDO system with a DIDO configurator at the Base Station, DIDOfeedback generator, and a DIDO feedback control channel for conveyingDIDO-specific configuration information from the users to the BaseStation.

a. Background

In multiple-input multiple-output (MIMO) systems, diversity schemes suchas orthogonal space-time block codes (OSTBC) (See V. Tarokh, H.Jafarkhani, and A. R. Calderbank, “Spacetime block codes from orthogonaldesigns,” IEEE Trans. Info. Th., vol. 45, pp. 1456-467, July 1999) orantenna selection (See R. W. Heath Jr., S. Sandhu, and A. J. Paulraj,“Antenna selection for spatial multiplexing systems with linearreceivers,” IEEE Trans. Comm., vol. 5, pp. 142-144, April 2001) areconceived to combat channel fading, providing increased link robustnessthat translates in better coverage. On the other hand, spatialmultiplexing (SM) enables transmission of multiple parallel data streamsas a means to enhance systems throughput. See G. J. Foschini, G. D.Golden, R. A. Valenzuela, and P. W. Wolniansky, “Simplified processingfor high spectral efficiency wireless communication employingmultielement arrays,” IEEE Jour. Select. Areas in Comm., vol. 17, no.11, pp. 1841-1852, November 1999. These benefits can be simultaneouslyachieved in MIMO systems, according to the theoreticaldiversity/multiplexing tradeoffs derived in L. Zheng and D. N. C. Tse,“Diversity and multiplexing: a fundamental tradeoff in multiple antennachannels,” IEEE Trans. Info. Th., vol. 49, no. 5, pp. 1073-1096, May2003. One practical implementation is to adaptively switch betweendiversity and multiplexing transmission schemes, by tracking thechanging channel conditions.

A number of adaptive MIMO transmission techniques have been proposedthus far. The diversity/multiplexing switching method in R. W. Heath andA. J. Paulraj, “Switching between diversity and multiplexing in MIMOsystems,” IEEE Trans. Comm., vol. 53, no. 6, pp. 962-968, June 2005, wasdesigned to improve BER (Bit Error Rate) for fixed rate transmission,based on instantaneous channel quality information. Alternatively,statistical channel information can be employed to enable adaptation asin S. Catreux, V. Erceg, D. Gesbert, and R. W. Heath. Jr., “Adaptivemodulation and MIMO coding for broadband wireless data networks,” IEEEComm. Mag., vol. 2, pp. 108-115, June 2002 (“Catreux”), resulting inreduced feedback overhead and number of control messages. The adaptivetransmission algorithm in Catreux was designed to enhance spectralefficiency for predefined target error rate in orthogonal frequencydivision multiplexing (OFDM) systems, based on channel time/frequencyselectivity indicators. Similar low feedback adaptive approaches havebeen proposed for narrowband systems, exploiting the channel spatialselectivity to switch between diversity schemes and spatialmultiplexing. See, e.g., A. Forenza, M. R. McKay, A. Pandharipande, R.W. Heath. Jr., and I. B. Collings, “Adaptive MIMO transmission forexploiting the capacity of spatially correlated channels,” accepted tothe IEEE Trans. on Veh. Tech., March 2007; M. R. McKay, I. B. Collings,A. Forenza, and R. W. Heath. Jr., “Multiplexing/beamforming switchingfor coded MIMO in spatially correlated Rayleigh channels,” accepted tothe IEEE Trans. on Veh. Tech., December 2007; A. Forenza, M. R. McKay,R. W. Heath. Jr., and I. B. Collings, “Switching between OSTBC andspatial multiplexing with linear receivers in spatially correlated MIMOchannels,” Proc. IEEE Veh. Technol. Conf., vol. 3, pp. 1387-1391, May2006; M. R. McKay, I. B. Collings, A. Forenza, and R. W. Heath Jr., “Athroughput-based adaptive MIMO BICM approach for spatially correlatedchannels,” to appear in Proc. IEEE ICC, June 2006

In this document, we extend the scope of the work presented in variousprior publications to DIDO-OFDM systems. See, e.g., R. W. Heath and A.J. Paulraj, “Switching between diversity and multiplexing in MIMOsystems,” IEEE Trans. Comm., vol. 53, no. 6, pp. 962-968, June 2005. S.Catreux, V. Erceg, D. Gesbert, and R. W. Heath Jr., “Adaptive modulationand MIMO coding for broadband wireless data networks,” IEEE Comm. Mag.,vol. 2, pp. 108-115, June 2002; A. Forenza, M. R. McKay, A.Pandharipande, R. W. Heath Jr., and I. B. Collings, “Adaptive MIMOtransmission for exploiting the capacity of spatially correlatedchannels,” IEEE Trans. on Veh. Tech., vol. 56, n. 2, pp. 619-630, March2007. M. R. McKay, I. B. Collings, A. Forenza, and R. W. Heath Jr.,“Multiplexing/beamforming switching for coded MIMO in spatiallycorrelated Rayleigh channels,” accepted to the IEEE Trans. on Veh.Tech., December 2007; A. Forenza, M. R. McKay, R. W. Heath Jr., and I.B. Collings, “Switching between OSTBC and spatial multiplexing withlinear receivers in spatially correlated MIMO channels,” Proc. IEEE Veh.Technol. Conf., vol. 3, pp. 1387-1391, May 2006. M. R. McKay, I. B.Collings, A. Forenza, and R. W. Heath Jr., “A throughput-based adaptiveMIMO BICM approach for spatially correlated channels,” to appear inProc. IEEE ICC, June 2006.

A novel adaptive DIDO transmission strategy is described herein thatswitches between different numbers of users, numbers of transmitantennas and transmission schemes based on channel quality informationas a means to improve the system performance. Note that schemes thatadaptively select the users in multiuser MIMO systems were alreadyproposed in M. Sharif and B. Hassibi, “On the capacity of MIMO broadcastchannel with partial side information,” IEEE Trans. Info. Th., vol. 51,p. 506522, February 2005; and W. Choi, A. Forenza, J. G. Andrews, and R.W. Heath Jr., “Opportunistic space division multiple access with beamselection,” to appear in IEEE Trans. on Communications. Theopportunistic space division multiple access (OSDMA) schemes in thesepublications, however, are designed to maximize the sum capacity byexploiting multi-user diversity and they achieve only a fraction of thetheoretical capacity of dirty paper codes, since the interference is notcompletely pre-canceled at the transmitter. In the DIDO transmissionalgorithm described herein block diagonalization is employed topre-cancel inter-user interference. The proposed adaptive transmissionstrategy, however, can be applied to any DIDO system, independently onthe type of pre-coding technique.

The present patent application describes an extension of the embodimentsof the invention described above and in the Prior Application,including, but not limited to the following additional features:

1. The training symbols of the Prior Application for channel estimationcan be employed by the wireless client devices to evaluate thelink-quality metrics in the adaptive DIDO scheme;

2. The base station receives signal characterization data from theclient devices as described in the Prior Application. In the currentembodiment, the signal characterization data is defined as link-qualitymetric used to enable adaptation;

3. The Prior Application describes a mechanism to select the number oftransmit antennas and users as well as defines throughput allocation.Moreover, different levels of throughput can be dynamically assigned todifferent clients as in the Prior Application. The current embodiment ofthe invention defines novel criteria related to this selection andthroughput allocation.

b. Embodiments of the Invention

The goal of the proposed adaptive DIDO technique is to enhance per-useror downlink spectral efficiency by dynamically allocating the wirelessresource in time, frequency and space to different users in the system.The general adaptation criterion is to increase throughput whilesatisfying the target error rate. Depending on the propagationconditions, this adaptive algorithm can also be used to improve the linkquality of the users (or coverage) via diversity schemes. The flowchartillustrated in FIG. 21 describes steps of the adaptive DIDO scheme.

The Base Station (BS) collects the channel state information (CSI) fromall the users in 2102. From the received CSI, the BS computes the linkquality metrics in time/frequency/space domains in 2104. These linkquality metrics are used to select the users to be served in the nexttransmission as well as the transmission mode for each of the users in2106. Note that the transmission modes consist of different combinationsof modulation/coding and DIDO schemes. Finally, the BS transmits data tothe users via DIDO precoding as in 2108.

At 2102, the Base Station collects the channel state information (CSI)from all the user devices. The CSI is used by the Base Station todetermine the instantaneous or statistical channel quality for all theuser devices at 2104. In DIDO-OFDM systems the channel quality (or linkquality metric) can be estimated in the time, frequency and spacedomains. Then, at 2106, the Base Station uses the link quality metric todetermine the best subset of users and transmission mode for the currentpropagation conditions. A set of DIDO transmission modes is defined ascombinations of DIDO schemes (i.e., antenna selection or multiplexing),modulation/coding schemes (MCSs) and array configuration. At 2108, datais transmitted to user devices using the selected number of users andtransmission modes.

In one embodiment, the mode selection is enabled by lookup tables (LUTs)pre-computed based on error rate performance of DIDO systems indifferent propagation environments. These LUTs map channel qualityinformation into error rate performance. To construct the LUTs, theerror rate performance of DIDO systems is evaluated in differentpropagation scenarios as a function of the SNR. From the error ratecurves, it is possible to compute the minimum SNR required to achievecertain pre-defined target error rate. We define this SNR requirement asSNR threshold. Then, the SNR thresholds are evaluated in differentpropagation scenarios and for different DIDO transmission modes andstored in the LUTs. For example, the SER results in FIGS. 24 and 26 canbe used to construct the LUTs. Then, from the LUTs, the Base Stationselects the transmission modes for the active users that increasethroughput while satisfying predefined target error rate. Finally, theBase Station transmits data to the selected users via DIDO pre-coding.Note that different DIDO modes can be assigned to different time slots,OFDM tones and DIDO substreams such that the adaptation may occur intime, frequency and space domains.

One embodiment of a system employing DIDO adaptation is illustrated inFIGS. 19-20. Several new functional units are introduced to enableimplementation of the proposed DIDO adaptation algorithms. Specifically,in one embodiment, a DIDO configurator 1910 performs a plurality offunctions including selecting the number of users, DIDO transmissionschemes (i.e., antenna selection or multiplexing), modulation/codingscheme (MCS), and array configurations based on the channel qualityinformation 1912 provided by user devices.

The user selector unit 1902 selects data associated with a plurality ofusers U₁-U_(M), based on the feedback information obtained by the DIDOconfigurator 1910, and provides this information each of the pluralityof coding modulation units 1904. Each coding modulation unit 1904encodes and modulates the information bits of each user and sends themto the mapping unit 1906. The mapping unit 1906 maps the input bits tocomplex symbols and sends it to the precoding unit 1908. Both the codingmodulation units 1904 and the mapping unit 1906 exploit the informationobtained from the DIDO configurator unit 1910 to choose the type ofmodulation/coding scheme to employ for each user. This information iscomputed by the DIDO configurator unit 1910 by exploiting the channelquality information of each of the users as provided by the feedbackunit 1912. The DIDO precoding unit 1908 exploits the informationobtained by the DIDO configurator unit 1910 to compute the DIDOprecoding weights and precoding the input symbols obtained from themapping units 1906. Each of the precoded data streams are sent by theDIDO precoding unit 1908 to the OFDM unit 1915 that computes the IFFTand adds the cyclic prefix. This information is sent to the D/A unit1916 that operates the digital to analog conversion and sends theresulting analog signal to the RF unit 1914. The RF unit 1914 upconvertsthe baseband signal to intermediate/radio frequency and send it to thetransmit antenna.

The RF units 2008 of each client device receive signals transmitted fromthe DIDO transmitter units 1914, downconverts the signals to basebandand provide the downconverted signals to the A/D units 2010. The A/Dunits 2010 then convert the signal from analog to digital and send it tothe OFDM units 2013. The OFDM units 2013 remove the cyclic prefix andcarries out the FFT to report the signal to the frequency domain. Duringthe training period the OFDM units 2013 send the output to the channelestimate unit 2004 that computes the channel estimates in the frequencydomain. Alternatively, the channel estimates can be computed in the timedomain. During the data period the OFDM units 2013 send the output tothe receiver unit 2002 which demodulates/decodes the signal to obtainthe data 2014. The channel estimate unit 2004 sends the channelestimates to the DIDO feedback generator unit 2006 that may quantize thechannel estimates and send it back to the transmitter via the feedbackcontrol channel 1912.

The DIDO configurator 1910 may use information derived at the BaseStation or, in a preferred embodiment, uses additionally the output of aDIDO Feedback Generator 2006 (see FIG. 20), operating at each userdevice. The DIDO Feedback Generator 2006 uses the estimated channelstate 2004 and/or other parameters like the estimated SNR at thereceiver to generate a feedback message to be input into the DIDOConfigurator 1910. The DIDO Feedback Generator 2006 may compressinformation at the receiver, may quantize information, and/or use somelimited feedback strategies known in the art.

The DIDO Configurator 1910 may use information recovered from a DIDOFeedback Control Channel 1912. The DIDO Feedback Control Channel 1912 isa logical or physical control channel that is used to send the output ofthe DIDO Feedback Generator 2006 from the user to the Base Station. Thecontrol channel 1912 may be implemented in any number of ways known inthe art and may be a logical or a physical control channel. As aphysical channel it may comprise a dedicated time/frequency slotassigned to a user. It may also be a random access channel shared by allusers. The control channel may be pre-assigned or it may be created bystealing bits in a predefined way from an existing control channel.

In the following discussion, results obtained through measurements withthe DIDO-OFDM prototype are described in real propagation environments.These results demonstrate the potential gains achievable in adaptiveDIDO systems. The performance of different order DIDO systems ispresented initially, demonstrating that it is possible to increase thenumber of antennas/user to achieve larger downlink throughput. The DIDOperformance as a function of user device's location is then described,demonstrating the need for tracking the changing channel conditions.Finally, the performance of DIDO systems employing diversity techniquesis described.

i. Performance of Different Order DIDO Systems

The performance of different DIDO systems is evaluated with increasingnumber of transmit antennas N=M, where M is the number of users. Theperformance of the following systems is compared: SISO, DIDO 2×2, DIDO4×4, DIDO 6×6 and DIDO 8×8. DIDO N×M refers to DIDO with N transmitantennas at the BS and M users.

FIG. 22 illustrates the transmit/receive antenna layout in a exemplaryresidential floor plan. The transmit antennas 2201 are placed in squaredarray configuration and the users are located around the transmit array.In FIG. 22, T indicates the “transmit” antennas and U refers to the“user devices” 2202.

Different antenna subsets are active in the 8-element transmit array,depending on the value of N chosen for different measurements. For eachDIDO order (N) the subset of antennas that covers the largest realestate for the fixed size constraint of the 8-element array was chosen.This criterion is expected to enhance the spatial diversity for anygiven value of N.

FIG. 23 shows the array configurations for different DIDO orders thatfit the available real estate (i.e., dashed line and outer walls). Thesquared dashed box has dimensions of 24″×24″, corresponding to ˜λ×λ atthe carrier frequency of 450 MHz.

Based on the comments related to FIG. 23 and with reference to FIG. 22,the performance of each of the following systems will now be defined andcompared:

SISO with T1 and U1 (2301)

DIDO 2×2 with T1,2 and U1,2 (2302)

DIDO 4×4 with T1,2,3,4 and U1,2,3,4 (2303)

DIDO 6×6 with T1,2,3,4,5,6 and U1,2,3,4,5,6 (2304)

DIDO 8×8 with T1,2,3,4,5,6,7,8 and U1,2,3,4,5,6,7,8 (2305)

FIG. 24 shows the SER, BER, SE (Spectral Efficiency) and goodputperformance as a function of the transmit (TX) power for the DIDOsystems described above, with 4-QAM and FEC (Forward Error Correction)rate of ½. Observe that the SER and BER performance degrades forincreasing values of N. This effect is due to two phenomena: for fixedTX power, the input power to the DIDO array is split between increasingnumber of users (or data streams); the spatial diversity decreases withincreasing number of users in realistic (spatially correlated) DIDOchannels.

To compare the relative performance of different order DIDO systems thetarget BER is fixed to 10⁻⁴ (this value may vary depending on thesystem) that corresponds approximately to SER=10⁻² as shown in FIG. 24.We refer to the TX power values corresponding to this target as TX powerthresholds (TPT). For any N, if the TX power is below the TPT, we assumeit is not possible to transmit with DIDO order N and we need to switchto lower order DIDO. Also, in FIG. 24, observe that the SE and goodputperformance saturate when the TX power exceeds the TPTs for any value ofN. From these results, an adaptive transmission strategy may be designedthat switches between different order DIDO to enhance SE or goodput forfixed predefined target error rate.

ii. Performance with Variable User Location

The goal of this experiment is to evaluate the DIDO performance fordifferent users' location, via simulations in spatially correlatedchannels. DIDO 2×2 systems are considered with 4QAM and an FEC rate of½. User 1 is at a broadside direction from the transmit array, whereasuser 2 changes locations from broadside to endfire directions asillustrated in FIG. 25. The transmit antennas are spaced ˜λ/2 andseparated ˜2.5λ from the users.

FIG. 26 shows the SER and per-user SE results for different locations ofuser device 2. The user device's angles of arrival (AOAs) range between0° and 90°, measured from the broadside direction of the transmit array.Observe that, as the user device's angular separation increases, theDIDO performance improves, due to larger diversity available in the DIDOchannel. Also, at target SER=10⁻² there is a 10 dB gap between the casesAOA2=0° and AOA2=90°. This result is consistent to the simulationresults obtained in FIG. 35 for an angle spread of 10°. Also, note thatfor the case of AOA1=AOA2=0° there may be coupling effects between thetwo users (due to the proximity of their antennas) that may vary theirperformance from the simulated results in FIG. 35.

iii. Preferred Scenario for DIDO 8×8

FIG. 24 illustrated that DIDO 8×8 yields a larger SE than lower orderDIDO at the expense of higher TX power requirement. The goal of thisanalysis is to show there are cases where DIDO 8×8 outperforms DIDO 2×2,not only in terms of peak spectral efficiency (SE), but also in terms ofTX power requirement (or TPT) to achieve that peak SE.

Note that, in i.i.d. (ideal) channels, there is ˜6 dB gap in TX powerbetween the SE of DIDO 8×8 and DIDO 2×2. This gap is due to the factthat DIDO 8×8 splits the TX power across eight data streams, whereasDIDO 2×2 only between two streams. This result is shown via simulationin FIG. 32.

In spatially correlated channels, however, the TPT is a function of thecharacteristics of the propagation environment (e.g., array orientation,user location, angle spread). For example, FIG. 35 shows ˜15 dB gap forlow angle spread between two different user device's locations. Similarresults are presented in FIG. 26 of the present application.

Similarly to MIMO systems, the performance of DIDO systems degrades whenthe users are located at endfire directions from the TX array (due tolack of diversity). This effect has been observed through measurementswith the current DIDO prototype. Hence, one way to show that DIDO 8×8outperforms DIDO 2×2 is to place the users at endfire directions withrespect to the DIDO 2×2 arrays. In this scenario, DIDO 8×8 outperformsDIDO 2×2 due to the higher diversity provided by the 8-antenna array.

In this analysis, consider the following systems:

System 1: DIDO 8×8 with 4-QAM (transmit 8 parallel data streams everytime slot);

System 2: DIDO 2×2 with 64-QAM (transmit to users X and Y every 4 timeslots). For this system we consider four combinations of TX and RXantenna locations: a) T1,T2 U1,2 (endfire direction); b) T3,T4 U3,4(endfire direction); c) T5,T6 U5,6 (˜30° from the endfire direction); d)T7,T8 U7,8 (NLOS (Non-Line of Sight));

System 3: DIDO 8×8 with 64-QAM; and

System 4: MISO 8×1 with 64-QAM (transmit to user X every 8 time slots).

For all these cases, an FEC rate of ¾ was used.

The users' locations are depicted in FIG. 27.

In FIG. 28 the SER results show a ˜15 dB gap between Systems 2 a and 2 cdue to different array orientations and user locations (similar to thesimulation results in FIG. 35). The first subplot in the second rowshows the values of TX power for which the SE curves saturate (i.e.corresponding to BER 1e-4). We observe that System 1 yields largerper-user SE for lower TX power requirement (˜5 dB less) than System 2.Also, the benefits of DIDO 8×8 versus DIDO 2×2 are more evident for theDL (downlink) SE and DL goodput due to multiplexing gain of DIDO 8×8over DIDO 2×2. System 4 has lower TX power requirement (8 dB less) thanSystem 1, due to the array gain of beamforming (i.e., MRC with MISO8×1). But System 4 yields only ⅓ of per-user SE compared to System 1.System 2 performs worse than System 1 (i.e., yields lower SE for largerTX power requirement). Finally, System 3 yields much larger SE (due tolarger order modulations) than System 1 for larger TX power requirement(˜15 dB).

From these results, the following conclusions may be drawn:

One channel scenario was identified for which DIDO 8×8 outperforms DIDO2×2 (i.e., yields larger SE for lower TX power requirement);

In this channel scenario, DIDO 8×8 yields larger per user SE and DL SEthan DIDO 2×2 and MISO 8×1; and

It is possible to further increase the performance of DIDO 8×8 by usinghigher order modulations (i.e., 64-QAM rather than 4-QAM) at the expenseof larger TX power requirements (˜15 dB more).

iv. DIDO with Antenna Selection

Hereafter, we evaluate the benefit of the antenna selection algorithmdescribed in R. Chen, R. W. Heath, and J. G. Andrews, “Transmitselection diversity for unitary precoded multiuser spatial multiplexingsystems with linear receivers,” accepted to IEEE Trans. on SignalProcessing, 2005. We present the results for one particular DIDO systemwith two users, 4-QAM and FEC rate of ½. The following systems arecompared in FIG. 27:

DIDO 2×2 with T1,2 and U1,2; and

DIDO 3×2 using antenna selection with T1,2,3 and U1,2.

The transmit antenna's and user device locations are the same as in FIG.27.

FIG. 29 shows that DIDO 3×2 with antenna selection may provide ˜5 dBgain compared to DIDO 2×2 systems (with no selection). Note that thechannel is almost static (i.e., no Doppler), so the selection algorithmsadapts to the path-loss and channel spatial correlation rather than thefast-fading. We should be seeing different gains in scenarios with highDoppler. Also, in this particular experiment it was observed that theantenna selection algorithm selects antennas 2 and 3 for transmission.

iv. SNR Thresholds for the LUTs

In section [0171] we stated that the mode selection is enabled by LUTs.The LUTs can be pre-computed by evaluating the SNR thresholds to achievecertain predefined target error-rate performance for the DIDOtransmission modes in different propagation environments. Hereafter, weprovide the performance of DIDO systems with and without antennaselection and variable number of users that can be used as guidelines toconstruct the LUTs. While FIGS. 24, 26, 28, 29 were derived frompractical measurements with the DIDO prototype, the following Figuresare obtained through simulations. The following BER results assume noFEC.

FIG. 30 shows the average BER performance of different DIDO precodingschemes in i.i.d. channels. The curve labeled as ‘no selection’ refersto the case when BD is employed. In the same figure the performance ofantenna selection (ASel) is shown for different number of extra antennas(with respect to the number of users). It is possible to see that as thenumber of extra antennas increases, ASel provides better diversity gain(characterized by the slope of the BER curve in high SNR regime),resulting in better coverage. For example, if we fix the target BER to10⁻² (practical value for uncoded systems), the SNR gain provided byASel increases with the number of antennas.

FIG. 31 shows the SNR gain of ASel as a function of the number of extratransmit antennas in i.i.d. channels, for different targets BER. It ispossible to see that, just by adding 1 or 2 antennas, ASel yieldssignificant SNR gains compared to BD. In the following sections, we willevaluate the performance of ASel only for the cases of 1 or 2 extraantennas and by fixing the target BER to 10⁻² (for uncoded systems).

FIG. 32 depicts the SNR thresholds as a function of the number of users(M) for BD and ASel with 1 and 2 extra antennas in i.i.d. channels. Weobserve that the SNR thresholds increase with M due to the largerreceive SNR requirement for larger number of users. Note that we assumefixed total transmit power (with variable number of transmit antennas)for any number of users. Moreover, FIG. 32 shows that the gain due toantenna selection is constant for any number of users in i.i.d.channels.

Hereafter, we show the performance of DIDO systems in spatiallycorrelated channels. We simulate each user's channel through theCOST-259 spatial channel model described in X. Zhuang, F. W. Vook, K. L.Baum, T. A. Thomas, and M. Cudak, “Channel models for link and systemlevel simulations,” IEEE 802.16 Broadband Wireless Access Working Group,September 2004. We generate single-cluster for each user. As a casestudy, we assume NLOS channels, uniform linear array (ULA) at thetransmitter, with element spacing of 0.5 lambda. For the case of 2-usersystem, we simulate the clusters with mean angles of arrival AOA1 andAOA2 for the first and second user, respectively. The AOAs are measuredwith respect to the broadside direction of the ULA. When more than twousers are in the system, we generate the users' clusters with uniformlyspaced mean AOAs in the range [−ϕ_(m),ϕ_(m)], where we define

$\begin{matrix}{\Phi_{M} = \frac{\Delta\;{\phi\left( {M - 1} \right)}}{2}} & (13)\end{matrix}$with K being the number of users and Δϕ is the angular separationbetween the users' mean AOAs. Note that the angular range [−ϕ_(m),ϕ_(m)]is centered at the 0° angle, corresponding to the broadside direction ofthe ULA. Hereafter, we study the BER performance of DIDO systems as afunction of the channel angle spread (AS) and angular separation betweenusers, with BD and ASel transmission schemes and different numbers ofusers.

FIG. 33 depicts the BER versus per-user average SNR for two userslocated at the same angular direction (i.e., AOA1=AOA2=0°, with respectto the broadside direction of the ULA), with different values of AS. Itis possible to see that as the AS increases the BER performance improvesand approaches the i.i.d. case. In fact, higher AS yields statisticallyless overlapping between the eigenmodes of the two users and betterperformance of the BD precoder.

FIG. 34 shows similar results as FIG. 33, but with higher angularseparation between the users. We consider AOA1=0° and AOA2=90° (i.e.,90° angular separation). The best performance is now achieved in the lowAS case. In fact, for the case of high angle separation, there is lessoverlapping between the users' eigenmodes when the angular spread islow. Interestingly, we observe that the BER performance in low AS isbetter than i.i.d. channels for the same reasons just mentioned.

Next, we compute the SNR thresholds, for target BER of 10⁻² in differentcorrelation scenarios. FIG. 35 plots the SNR thresholds as a function ofthe AS for different values of the mean AOAs of the users. For lowusers' angular separation reliable transmissions with reasonable SNRrequirement (i.e., 18 dB) are possible only for channels characterizedby high AS. On the other hand, when the users are spatially separated,less SNR is required to meet the same target BER.

FIG. 36 shows the SNR threshold for the case of five users. The users'mean AOAs are generated according to the definition in (13), withdifferent values of angular separation Δϕ. We observe that for Δϕ=0° andAS<15°, BD performs poorly due to the small angle separation betweenusers, and the target BER is not satisfied. For increasing AS the SNRrequirement to meet the fixed target BER decreases. On the other end,for Δϕ=30°, the smallest SNR requirement is obtained at low AS,consistently to the results in FIG. 35. As the AS increases, the SNRthresholds saturate to the one of i.i.d. channels. Note that Δϕ=30″ with5 users corresponds to the AOA range of [−60°, 60°], that is typical forbase stations in cellular systems with 120° sectorized cells.

Next, we study the performance of ASel transmission scheme in spatiallycorrelated channels. FIG. 37 compares the SNR threshold of BD and ASel,with 1 and 2 extra antennas, for two user case. We consider twodifferent cases of angular separation between users: {AOA1=0°,AOA2=0°}and {AOA1=0°,AOA2=90°}. The curves for BD scheme (i.e., no antennaselection) are the same as in FIG. 35. We observe that ASel yields 8 dBand 10 dB SNR gains with 1 and 2 extra antennas, respectively, for highAS. As the AS decreases, the gain due to ASel over BD becomes smallerdue to the reduced number of degrees of freedom in the MIMO broadcastchannel. Interestingly, for AS=0° (i.e., close to LOS channels) and thecase {AOA1=0°,AOA2=90°}, ASel does not provide any gain due to the luckof diversity in the space domain. FIG. 38 shows similar results as FIG.37, but for five user case.

We compute the SNR thresholds (assuming usual target BER of 10⁻²) as afunction of the number of users in the system (M), for both BD and ASeltransmission schemes. The SNR thresholds correspond to the average SNR,such that the total transmit power is constant for any M. We assumemaximum separation between the mean AOAs of each user's cluster withinthe azimuth range [−ϕ_(m),ϕ_(m)]=[−60°, 60°]. Then, the angularseparation between users is Δϕ=120°/(M−1).

FIG. 39 shows the SNR thresholds for BD scheme with different values ofAS. We observe that the lowest SNR requirement is obtained for AS=0.1°(i.e., low angle spread) with relatively small number of users (i.e.,K≤20), due to the large angular separation between users. For M>50,however, the SNR requirement is way above 40 dB, since Δϕ is very small,and BD is impractical. Moreover, for AS>10° the SNR thresholds remainalmost constant for any M, and the DIDO system in spatially correlatedchannels approaches the performance of i.i.d. channels.

To reduce the values of the SNR thresholds and improve the performanceof the DIDO system we apply ASel transmission scheme. FIG. 40 depictsthe SNR thresholds in spatially correlated channels with AS=0.1° for BDand ASel with 1 and 2 extra antennas. For reference we report also thecurves for the i.i.d. case shown in FIG. 32. It is possible to see that,for low number of users (i.e., M≤10), antenna selection does not helpreducing the SNR requirement due to the lack of diversity in the DIDObroadcast channel. As the number of users increases, ASel benefits frommultiuser diversity yielding SNR gains (i.e., 4 dB for M=20). Moreover,for M≤20, the performance of ASel with 1 or 2 extra antennas in highlyspatially correlated channels is the same.

We then compute the SNR thresholds for two more channel scenarios: AS=5°in FIG. 41 and AS=10° in FIG. 42. FIG. 41 shows that ASel yields SNRgains also for relatively small number of users (i.e., M≤10) as opposedto FIG. 40, due to the larger angle spread. For AS=10° the SNRthresholds reduce further and the gains due to ASel get higher, asreported in FIG. 42.

Finally, we summarize the results presented so far for correlatedchannels. FIG. 43 and FIG. 44 show the SNR thresholds as a function ofthe number of users (M) and angle spread (AS) for BD and ASel schemes,with 1 and 2 extra antennas, respectively. Note that the case of AS=30°corresponds actually to i.i.d. channels, and we used this value of AS inthe plot only, for graphical representation. We observe that, while BDis affected by the channel spatial correlation, ASel yields almost thesame performance for any AS. Moreover, for AS=0.1°, ASel performssimilarly to BD for low M, whereas outperforms BD for large M (i.e.,M≥20), due to multiuser diversity.

FIG. 49 compares the performance of different DIDO schemes in terms ofSNR thresholds. The DIDO schemes considered are: BD, ASel, BD witheigenmode selection (BD-ESel) and maximum ratio combining (MRC). Notethat MRC, does not pre-cancel interference at the transmitter (unlikethe other methods), but does provide larger gain in case the users arespatially separated. In FIG. 49 we plot the SNR threshold for targetBER=10−2 for DIDO N×2 systems when the two users are located at −30° and30° from the broadside direction of the transmit array, respectively. Weobserve that for low AS the MRC scheme provides 3 dB gain compared tothe other schemes since the users' spatial channels are well separatedand the effect of inter-user interference is low. Note that the gain ofMRC over DIDO N×2 are due to array gain. For AS larger than 20° theQR-ASel scheme outperforms the other and yields about 10 dB gaincompared to BD 2×2 with no selection. QR-ASel and BD-ESel provide aboutthe same performance for any value of AS.

Described above is a novel adaptive transmission technique for DIDOsystems. This method dynamically switches between DIDO transmissionmodes to different users to enhance throughput for fixed target errorrate. The performance of different order DIDO systems was measured indifferent propagation conditions and it was observed that significantgains in throughput may be achieved by dynamically selecting the DIDOmodes and number of users as a function of the propagation conditions.

Pre-Compensation of Frequency and Phase Offset a. Background

As previously described, wireless communication systems use carrierwaves to convey information. These carrier waves are usually sinusoidsthat are amplitude and/or phase modulated in response to information tobe transmitted. The nominal frequency of the sinusoid is known as thecarrier frequency. To create this waveform, the transmitter synthesizesone or more sinusoids and uses upconversion to create a modulated signalriding on a sinusoid with the prescribed carrier frequency. This may bedone through direct conversion where the signal is directly modulated onthe carrier or through multiple upconversion stages. To process thiswaveform, the receiver must demodulate the received RF signal andeffectively remove the modulating carrier. This requires that thereceiver synthesize one or more sinusoidal signals to reverse theprocess of modulation at the transmitter, known as downconversion.Unfortunately, the sinusoidal signals generated at the transmitter andreceiver are derived from different reference oscillators. No referenceoscillator creates a perfect frequency reference; in practice there isalways some deviation from the true frequency.

In wireless communication systems, the differences in the outputs of thereference oscillators at the transmitter and receivers create thephenomena known as carrier frequency offset, or simply frequency offset,at the receiver. Essentially there is some residual modulation in thereceived signal (corresponding to the difference in the transmit andreceive carriers), which occurs after downconversion. This createsdistortion in the received signal resulting in higher bit error ratesand lower throughput.

There are different techniques for dealing with carrier frequencyoffset. Most approaches estimate the carrier frequency offset at thereceiver and then apply a carrier frequency offset correction algorithm.The carrier frequency offset estimation algorithm may be blind usingoffset QAM (T. Fusco and M. Tanda, “Blind Frequency-offset Estimationfor OFDM/OQAM Systems,” IEEE Transactions on Signal Processing, vol. 55,pp. 1828-1838,2007); periodic properties (E. Serpedin, A. Chevreuil, G.B. Giannakis, and P. Loubaton, “Blind channel and carrier frequencyoffset estimation using periodic modulation precoders,” IEEETransactions on Signal Processing, vol. 48, no. 8, pp. 2389-2405, August2000); or the cyclic prefix in orthogonal frequency divisionmultiplexing (OFDM) structure approaches (J. J. van de Beek, M. Sandell,and P. O. Borjesson, “ML estimation of time and frequency offset in OFDMsystems,” IEEE Transactions on Signal Processing, vol. 45, no. 7, pp.1800-1805, July 1997; U. Tureli, H. Liu, and M. D. Zoltowski, “OFDMblind carrier offset estimation: ESPRIT,” IEEE Trans. Commun., vol. 48,no. 9, pp. 1459-1461, September 2000; M. Luise, M. Marselli, and R.Reggiannini, “Low-complexity blind carrier frequency recovery for OFDMsignals over frequency-selective radio channels,” IEEE Trans. Commun.,vol. 50, no. 7, pp. 1182-1188, July 2002).

Alternatively special training signals may be utilized including arepeated data symbol (P. H. Moose, “A technique for orthogonal frequencydivision multiplexing frequency offset correction,” IEEE Trans. Commun.,vol. 42, no. 10, pp. 2908-2914, October 1994); two different symbols (T.M. Schmidl and D. C. Cox, “Robust frequency and timing synchronizationfor OFDM,” IEEE Trans. Commun., vol. 45, no. 12, pp. 1613-1621, December1997); or periodically inserted known symbol sequences (M. Luise and R.Reggiannini, “Carrier frequency acquisition and tracking for OFDMsystems,” IEEE Trans. Commun., vol. 44, no. 11, pp. 1590-1598, November1996). The correction may occur in analog or in digital. The receivercan also use carrier frequency offset estimation to precorrect thetransmitted signal to eliminate offset. Carrier frequency offsetcorrection has been studied extensively for multicarrier and OFDMsystems due to their sensitivity to frequency offset (J. J. van de Beek,M. Sandell, and P. O. Borjesson, “ML estimation of time and frequencyoffset in OFDM systems,” Signal Processing, IEEE Transactions on [seealso Acoustics, Speech, and Signal Processing, IEEE Transactions on],vol. 45, no. 7, pp. 1800-1805, July 1997; U. Tureli, H. Liu, and M. D.Zoltowski, “OFDM blind carrier offset estimation: ESPRIT,” IEEE Trans.Commun., vol. 48, no. 9, pp. 1459-1461, September 2000; T. M. Schmidland D. C. Cox, “Robust frequency and timing synchronization for OFDM,”IEEE Trans. Commun., vol. 45, no. 12, pp. 1613-1621, December 1997; M.Luise, M. Marselli, and R. Reggiannini, “Low-complexity blind carrierfrequency recovery for OFDM signals over frequency-selective radiochannels,” IEEE Trans. Commun., vol. 50, no. 7, pp. 1182-1188, July2002).

Frequency offset estimation and correction is an important issue formulti-antenna communication systems, or more generally MIMO (multipleinput multiple output) systems. In MIMO systems where the transmitantennas are locked to one frequency reference and the receivers arelocked to another frequency reference, there is a single offset betweenthe transmitter and receiver. Several algorithms have been proposed totackle this problem using training signals (K. Lee and J. Chun,“Frequency-offset estimation for MIMO and OFDM systems using orthogonaltraining sequences,” IEEE Trans. Veh. Technol., vol. 56, no. 1, pp.146-156, January 2007; M. Ghogho and A. Swami, “Training design formultipath channel and frequency offset estimation in MIMO systems,” IEEETransactions on Signal Processing, vol. 54, no. 10, pp. 3957-3965,October 2006, and adaptive tracking C. Oberli and B. Daneshrad, “Maximumlikelihood tracking algorithms for MIMOOFDM,” in Communications, 2004IEEE International Conference on, vol. 4, Jun. 20-24, 2004, pp.2468-2472). A more severe problem is encountered in MIMO systems wherethe transmit antennas are not locked to the same frequency reference butthe receive antennas are locked together. This happens practically inthe uplink of a spatial division multiple access (SDMA) system, whichcan be viewed as a MIMO system where the different users correspond todifferent transmit antennas. In this case the compensation of frequencyoffset is much more complicated. Specifically, the frequency offsetcreates interference between the different transmitted MIMO streams. Itcan be corrected using complex joint estimation and equalizationalgorithms (A. Kannan, T. P. Krauss, and M. D. Zoltowski, “Separation ofcochannel signals under imperfect timing and carrier synchronization,”IEEE Trans. Veh. Technol., vol. 50, no. 1, pp. 79-96, January 2001), andequalization followed by frequency offset estimation (T. Tang and R. W.Heath, “Joint frequency offset estimation and interference cancellationfor MIMO-OFDM systems [mobile radio],” 2004. VTC2004-Fall. 2004 IEEE60^(th) Vehicular Technology Conference, vol. 3, pp. 1553-1557, Sep.26-29, 2004; X. Dai, “Carrier frequency offset estimation for OFDM/SDMAsystems using consecutive pilots,” IEEE Proceedings-Communications, vol.152, pp. 624-632, Oct. 7, 2005). Some work has dealt with the relatedproblem of residual phase off-set and tracking error, where residualphase offsets are estimated and compensated after frequency offsetestimation, but this work only consider the uplink of an SDMA OFDMAsystem (L. Haring, S. Bieder, and A. Czylwik, “Residual carrier andsampling frequency synchronization in multiuser OFDM systems,” 2006. VTC2006-Spring. IEEE 63rd Vehicular Technology Conference, vol. 4, pp.1937-1941, 2006). The most severe case in MIMO systems occurs when alltransmit and receive antennas have different frequency references. Theonly available work on this topic only deals with asymptotic analysis ofestimation error in flat fading channels (O. Besson and P. Stoica, “Onparameter estimation of MIMO flat-fading channels with frequencyoffsets,” Signal Processing, IEEE Transactions on [see also Acoustics,Speech, and Signal Processing, IEEE Transactions on], vol. 51, no. 3,pp. 602-613, March 2003).

A case that has not been significantly investigated occurs when thedifferent transmit antennas of a MIMO system do not have the samefrequency reference and the receive antennas process the signalsindependently. This happens in what is known as a distributed inputdistributed-output (DIDO) communication system, also called the MIMObroadcast channel in the literature. DIDO systems consist of one accesspoint with distributed antennas that transmit parallel data streams (viaprecoding) to multiple users to enhance downlink throughput, whileexploiting the same wireless resources (i.e., same slot duration andfrequency band) as conventional SISO systems. Detailed description ofDIDO systems was presented in, S. G. Perlman and T. Cotter, “System andmethod for distributed input-distributed output wirelesscommunications,” United States Patent Application 20060023803, July2004. There are many ways to implement DIDO precoders. One solution isblock diagonalization (BD) described in, for example, Q. H. Spencer, A.L. Swindlehurst, and M. Haardt, “Zero-forcing methods for downlinkspatial multiplexing in multiuser MIMO channels,” IEEE Trans. Sig.Proc., vol. 52, pp. 461-471, February 2004; K. K. Wong, R. D. Murch, andK. B. Letaief, “A joint-channel diagonalization for multiuser MIMOantenna systems,” IEEE Trans. Wireless Comm., vol. 2, pp. 773-786, July2003; L. U. Choi and R. D. Murch, “A transmit preprocessing techniquefor multiuser MIMO systems using a decomposition approach,” IEEE Trans.Wireless Comm., vol. 3, pp. 20-24, January 2004; Z. Shen, J. G. Andrews,R. W. Heath, and B. L. Evans, “Low complexity user selection algorithmsfor multiuser MIMO systems with block diagonalization,” accepted forpublication in IEEE Trans. Sig. Proc., September 2005; Z. Shen, R. Chen,J. G. Andrews, R. W. Heath, and B. L. Evans, “Sum capacity of multiuserMIMO broadcast channels with block diagonalization,” submitted to IEEETrans. Wireless Comm., October 2005; R. Chen, R. W. Heath, and J. G.Andrews, “Transmit selection diversity for unitary precoded multiuserspatial multiplexing systems with linear receivers,” accepted to IEEETrans. on Signal Processing, 2005.

In DIDO systems, transmit precoding is used to separate data streamsintended for different users. Carrier frequency offset causes severalproblems related to the system implementation when the transmit antennaradio frequency chains do not share the same frequency reference. Whenthis happens, each antenna is effectively transmits at a slightlydifferent carrier frequency. This destroys the integrity of the DIDOprecoder resulting in each user experiencing extra interference.Proposed below are several solutions to this problem. In one embodimentof the solution, the DIDO transmit antennas share a frequency referencethrough a wired, optical, or wireless network. In another embodiment ofthe solution, one or more users estimate the frequency offsetdifferences (the relative differences in the offsets between pairs ofantennas) and send this information back to the transmitter. Thetransmitter then precorrects for the frequency offset and proceeds withthe training and precoder estimation phase for DIDO. There is a problemwith this embodiment when there are delays in the feedback channel. Thereason is that there may be residual phase errors created by thecorrection process that are not accounted for in the subsequent channelestimation. To solve this problem, one additional embodiment uses anovel frequency offset and phase estimator that can correct this problemby estimating the delay. Results are presented based both on simulationsand practical measurements carried out with a DIDO-OFDM prototype.

The frequency and phase offset compensation method proposed in thisdocument may be sensitive to estimation errors due to noise at thereceiver. Hence, one additional embodiment proposes methods for time andfrequency offset estimation that are robust also under low SNRconditions.

There are different approaches for performing time and frequency offsetestimation. Because of its sensitivity to synchronization errors, manyof these approaches were proposed specifically for the OFDM waveform.

The algorithms typically do not exploit the structure of the OFDMwaveform thus they are generic enough for both single carrier andmulticarrier waveforms. The algorithm described below is among a classof techniques that employ known reference symbols, e.g. training data,to aid in synchronization. Most of these methods are extensions ofMoose's frequency offset estimator (see P. H. Moose, “A technique fororthogonal frequency division multiplexing frequency offset correction,”IEEE Trans. Commun., vol. 42, no. 10, pp. 2908-2914, October 1994.).Moose proposed to use two repeated training signals and derived thefrequency offset using the phase difference between both receivedsignals. Moose's method can only correct for the fractional frequencyoffset. An extension of the Moose method was proposed by Schmidl and Cox(T. M. Schmidl and D. C. Cox, “Robust frequency and timingsynchronization for OFDM,” IEEE Trans. Commun., vol. 45, no. 12, pp.1613-1621, December 1997.). Their key innovation was to use one periodicOFDM symbol along with an additional differentially encoded trainingsymbol. The differential encoding in the second symbol enables integeroffset correction. Coulson considered a similar setup as described in T.M. Schmidl and D. C. Cox, “Robust frequency and timing synchronizationfor OFDM,” IEEE Trans. Commun., vol. 45, no. 12, pp. 1613-1621, December1997, and provided a detailed discussion of algorithms and analysis asdescribed in A. J. Coulson, “Maximum likelihood synchronization for OFDMusing a pilot symbol: analysis,” IEEE J. Select. Areas Commun., vol. 19,no. 12, pp. 2495-2503, December 2001; A. J. Coulson, “Maximum likelihoodsynchronization for OFDM using a pilot symbol: algorithms,” IEEE J.Select. Areas Commun., vol. 19, no. 12, pp. 2486-2494, December 2001.One main difference is that Coulson uses repeated maximum lengthsequences to provide good correlation properties. He also suggests usingchirp signals because of their constant envelope properties in the timeand frequency domains. Coulson considers several practical details butdoes not include integer estimation. Multiple repeated training signalswere considered by Minn et. al. in H. Minn, V. K. Bhargava, and K. B.Letaief, “A robust timing and frequency synchronization for OFDMsystems,” IEEE Trans. Wireless Commun., vol. 2, no. 4, pp. 822-839, July2003, but the structure of the training was not optimized. Shi andSerpedin show that the training structure has some optimality form theperspective of frame synchronization (K. Shi and E. Serpedin, “Coarseframe and carrier synchronization of OFDM systems: a new metric andcomparison,” IEEE Trans. Wireless Commun., vol. 3, no. 4, pp. 1271-1284,July 2004). One embodiment of the invention uses the Shi and Serpedinapproach to perform frame synchronization and fractional frequencyoffset estimation.

Many approaches in the literature focus on frame synchronization andfractional frequency offset correction. Integer offset correction issolved using an additional training symbol as in T. M. Schmidl and D. C.Cox, “Robust frequency and timing synchronization for OFDM,” IEEE Trans.Commun., vol. 45, no. 12, pp. 1613-1621, December 1997. For example,Morrelli et. al. derived an improved version of T. M. Schmidl and D. C.Cox, “Robust frequency and timing synchronization for OFDM,” IEEE Trans.Commun., vol. 45, no. 12, pp. 1613-1621, December 1997, in M. Morelli,A. N. D'Andrea, and U. Mengali, “Frequency ambiguity resolution in OFDMsystems,” IEEE Commun. Lett., vol. 4, no. 4, pp. 134-136, April 2000. Analternative approach using a different preamble structure was suggestedby Morelli and Mengali (M. Morelli and U. Mengali, “An improvedfrequency offset estimator for OFDM applications,” IEEE Commun. Lett.,vol. 3, no. 3, pp. 75-77, March 1999). This approach uses thecorrelations between M repeated identical training symbols to increasethe range of the fractional frequency offset estimator by a factor of M.This is the best linear unbiased estimator and accepts a large offset(with proper design) but does not provide good timing synchronization.

System Description

One embodiment of the invention uses pre-coding based on channel stateinformation to cancel frequency and phase offsets in DIDO systems. SeeFIG. 11 and the associated description above for a description of thisembodiment.

In one embodiment of the invention, each user employs a receiverequipped with frequency offset estimator/compensator. As illustrated inFIG. 45, in one embodiment of the invention, a system including thereceiver includes a plurality of RF units 4508, a correspondingplurality of A/D units 4510, a receiver equipped with a frequency offsetestimator/compensator 4512 and a DIDO feedback generator unit 4506.

The RF units 4508 receive signals transmitted from the DIDO transmitterunits, downconvert the signals to baseband and provide the downconvertedsignals to the A/D units 4510. The A/D units 4510 then convert thesignal from analog to digital and send it to the frequency offsetestimator/compensator units 4512. The frequency offsetestimator/compensator units 4512 estimate the frequency offset andcompensate for it, as described herein, and then send the compensatedsignal to the OFDM units 4513. The OFDM units 4513 remove the cyclicprefix and operate the Fast Fourier Transform (FFT) to report the signalto the frequency domain. During the training period the OFDM units 4513send the output to the channel estimate unit 4504 that computes thechannel estimates in the frequency domain. Alternatively, the channelestimates can be computed in the time domain. During the data period theOFDM units 4513 send the output to the DIDO receiver unit 4502 whichdemodulates/decodes the signal to obtain the data. The channel estimateunit 4504 sends the channel estimates to the DIDO feedback generatorunit 4506 that may quantize the channel estimates and send them back tothe transmitter via the feedback control channel, as illustrated.

Description of One Embodiment of an Algorithm for a DIDO 2×2 Scenario

Described below are embodiments of an algorithm for frequency/phaseoffset compensation in DIDO systems. The DIDO system model is initiallydescribed with and without frequency/phase offsets. For the sake of thesimplicity, the particular implementation of a DIDO 2×2 system isprovided. However, the underlying principles of the invention may alsobe implemented on higher order DIDO systems.

DIDO System Model w/o Frequency and Phase Offset

The received signals of DIDO 2×2 can be written for the first user asr ₁ [t]=h ₁₁(w ₁₁ x ₁ [t]+w ₂₁ x ₂ [t])+h ₁₂(w ₁₂ x ₁ [t]+w ₂₂ x ₂[t])  (1)and for the second user asr ₂ [t]=h ₂₁(w ₁₁ x ₁ [t]+w ₂₁ x ₂ [t])+h ₂₂(w ₁₂ x ₁ [t]+w ₂₂ x ₂[t])  (2)where t is the discrete time index, h_(mn) hand w_(mn) are the channeland the DIDO precoding weights between the m-th user and n-th transmitantenna, respectively, and x_(m) is the transmit signal to user m. Notethat h_(mn) and w_(mn) are not a function of t since we assume thechannel is constant over the period between training and datatransmission.

In the presence of frequency and phase offset, the received signals areexpressed asr ₁ [t]=e ^(j(ω) ^(U1) ^(−ω) ^(T1) ^()T) ^(s) ^((t−t) ¹¹ ⁾ h ₁₁(w ₁₁ x ₁[t]+w ₂₁ x ₂ [t])+e ^(j(ω) ^(U1) ^(−ω) ^(T2) ^()T) ^(s) ^((t−t) ¹² ⁾ h₁₂(w ₁₂ x ₁ [t]+w ₂₂ x ₂ [t])  (3)andr ₂ [t]=e ^(j(ω) ^(U1) ^(−ω) ^(T1) ^()T) ^(s) ^((t−t) ²¹ ⁾ h ₂₁(w ₁₁ x ₁[t]+w ₂₁ x ₂ [t])+e ^(j(ω) ^(U1) ^(−ω) ^(T2) ^()T) ^(s) ^((t−t) ²² ⁾ h₂₂(w ₁₂ x ₁ [t]+w ₂₂ x ₂ [t])  (4)where T_(s) is the symbol period, ω_(Tn)=2Πf_(Tn) for the n-th transmitantenna, ω_(Um)=2Πf_(Um) for the m-th user, and f_(Tn) and f_(Um) arethe actual carrier frequencies (affected by offset) for the n-thtransmit antenna and m-th user, respectively. The values t_(mn) denoterandom delays that cause phase offset over the channel h_(mn). FIG. 46depicts the DIDO 2×2 system model.

For the time being, we use the following definitions:Δω_(mn)=ω_(Um)−ω_(Tn)  (5)to denote the frequency offset between the m-th user and the n-thtransmit antenna.

A method according to one embodiment of the invention is illustrated inFIG. 47. The method includes the following general steps (which includesub-steps, as illustrated): training period for frequency offsetestimation 4701; training period for channel estimation 4702; datatransmission via DIDO precoding with compensation 4703. These steps aredescribed in detail below.

(a) Training Period for Frequency Offset Estimation (4701)

During the first training period the base station sends one or moretraining sequences from each transmit antennas to one of the users (4701a). As described herein “users” are wireless client devices. For theDIDO 2×2 case, the signal received by the m-th user is given byr _(m) [t]=e ^(jΔω) ^(m1) ^(T) ^(s) ^((t−t) ^(m1) ⁾ h _(m1) p ₁ [t]+e^(jΔω) ^(m2) ^(T) ^(s) ^((t−t) ^(m2) ⁾ h _(m2) p ₂ [t]  (6)where p₁ and p₂ are the training sequences transmitted from the firstand second antennas, respectively.

The m-th user may employ any type of frequency offset estimator (i.e.,convolution by the training sequences) and estimates the offsets Δω_(m1)and Δω_(m2). Then, from these values the user computes the frequencyoffset between the two transmit antennas asΔω_(T)=Δω_(m2)−Δω_(m1)=ω_(T1)−ω_(T2)  (7)Finally, the value in (7) is fed back to the base station (4701 b).

Note that p₁ and p₂ in (6) are designed to be orthogonal, so that theuser can estimate Δω_(m1) and Δω_(m2). Alternatively, in one embodiment,the same training sequence is used over two consecutive time slots andthe user estimates the offset from there. Moreover, to improve theestimate of the offset in (7) the same computations described above canbe done for all users of the DIDO systems (not just for the m-th user)and the final estimate may be the (weighted) average of the valuesobtained from all users. This solution, however, requires morecomputational time and amount of feedback. Finally, updates of thefrequency offset estimation are needed only if the frequency offsetvaries over time. Hence, depending on the stability of the clocks at thetransmitter, this step 4701 of the algorithm can be carried out on along-term basis (i.e., not for every data transmission), resulting inreduction of feedback overhead.

-   -   (a) Training Period for Channel Estimation (4702)    -   (b) During the second training period, the base station first        obtains the frequency offset feedback with the value in (7) from        the m-th user or from the plurality of users. The value in (7)        is used to pre-compensate for the frequency offset at the        transmit side. Then, the base station sends training data to all        the users for channel estimation (4702 a).

For DIDO 2×2 systems, the signal received at the first user is given byr ₁ [t]=e ^(jΔω) ¹¹ ^(T) ^(s) ^((t−{tilde over (t)}) ¹¹ ⁾ h ₁₁ p ₁ [t]+e^(jΔω) ¹² ^(T) ^(s) ^((t−{tilde over (t)}) ¹² ⁾ h ₁₂ e ^(−jΔω) ^(T) ^(T)^(s) ^(t) p ₂ [t]  (8)and at the second user byr ₂ [t]=e ^(jΔω) ²¹ ^(T) ^(s) ^((t−{tilde over (t)}) ²¹ ⁾ h ₂₁ p ₁ [t]+e^(jΔω) ²² ^(T) ^(s) ^((t−{tilde over (t)}) ²² ⁾ h ₂₂ e ^(−jΔω) ^(T) ^(T)^(s) ^(t) p ₂ [t]  (9)where {tilde over (t)}_(mn)=t_(mn)+Δt and Δt is random or known delaybetween the first and second transmissions of the base station.Moreover, p₁ and p₂ are the training sequences transmitted from thefirst and second antennas, respectively, for frequency offset andchannel estimation.

Note that the pre-compensation is applied only to the second antennas inthis embodiment.

Expanding (8) we obtainr ₁ [t]=e ^(jΔω) ¹¹ ^(T) ^(s) ^(t) e ^(jθ) ¹¹ [h ₁₁ p ₁ [t]+e ^(j(θ) ¹²^(−θ) ¹¹ ⁾ h ₁₂ p ₂ [t]]  (10)and similarly for the second userr ₂ [t]=e ^(jΔω) ²¹ ^(T) ^(s) ^(t) e ^(jθ) ²¹ [h ₂₁ p ₁ [t]+e ^(j(θ) ²²^(−θ) ²¹ ⁾ h ₂₂ p ₂ [t]]  (11)where θ_(mn)=−Δω_(mn)T_(s){tilde over (t)}_(mn).

At the receive side, the users compensate for the residual frequencyoffset by using the training sequences p₁ and p₂. Then the usersestimate via training the vector channels (4702 b)

$\begin{matrix}{{h_{1} = \begin{bmatrix}h_{11} \\{e^{j{({\theta_{12} - \theta_{11}})}}h_{12}}\end{bmatrix}}{h_{2} = \begin{bmatrix}h_{21} \\{e^{j{({\theta_{22} - \theta_{21}})}}h_{22}}\end{bmatrix}}} & (12)\end{matrix}$

These channel in (12) or channel state information (CSI) is fed back tothe base station (4702 b) that computes the DIDO precoder as describedin the following subsection.

-   -   (c) DIDO Precoding with Pre-compensation (4703)

The base station receives the channel state information (CSI) in (12)from the users and computes the precoding weights via blockdiagonalization (BD) (4703 a), such thatw ₁ ^(T) h ₂=0,w ₂ ^(T) h ₁=0  (13)where the vectors h₁ are defined in (12) and w_(m)=[w_(m1),w_(m2)]. Notethat the invention presented in this disclosure can be applied to anyother DIDO precoding method besides BD. The base station alsopre-compensates for the frequency offset by employing the estimate in(7) and phase offset by estimating the delay (Δt₀) between the secondtraining transmission and the current transmission (4703 a). Finally,the base station sends data to the users via the DIDO precoder (4703 b)

After this transmit processing, the signal received at user 1 is givenby

$\begin{matrix}\begin{matrix}{{r_{1}\lbrack t\rbrack} = {e^{j\;{\Delta\omega}_{11}{T_{s}{({t - {\overset{\sim}{t}}_{11} - {\Delta\; t_{o}}})}}}{h_{11}\left\lbrack {{w_{11}{x_{1}\lbrack t\rbrack}} + {w_{21}{x_{2}\lbrack t\rbrack}}} \right\rbrack}}} \\{= {e^{j\;{\Delta\omega}_{12}{T_{s}{({t - {\overset{\sim}{t}}_{12} - {\Delta\; t_{o}}})}}}h_{12}{e^{{- j}\;{\Delta\omega}_{T}{T_{S}{({t - {\Delta\; t_{o}}})}}}\begin{bmatrix}{{w_{12}{x_{1}\lbrack t\rbrack}} +} \\{w_{22}{x_{2}\lbrack t\rbrack}}\end{bmatrix}}}} \\{= {{\gamma_{1}\lbrack t\rbrack}\begin{bmatrix}{{h_{11}\left( {{w_{11}{x_{1}\lbrack t\rbrack}} + {w_{21}{x_{2}\lbrack t\rbrack}}} \right)} +} \\{e^{{j{({{{\Delta\omega}_{11}t_{11}} - {{\Delta\omega}_{12}t_{12}}})}}T_{s}}{h_{12}\left( {{w_{12}{x_{1}\lbrack t\rbrack}} + {w_{22}{x_{2}\lbrack t\rbrack}}} \right)}}\end{bmatrix}}} \\{= {{\gamma_{1}\lbrack t\rbrack}\begin{bmatrix}{{\left( {{h_{11}w_{11}} + {e^{j{({\theta_{12} - \theta_{11}})}}h_{12}w_{12}}} \right){x_{1}\lbrack t\rbrack}} +} \\{\left( {{h_{11}w_{21}} + {e^{j{({\theta_{12} - \theta_{11}})}}h_{12}w_{22}}} \right){x_{2}\lbrack t\rbrack}}\end{bmatrix}}}\end{matrix} & (14)\end{matrix}$where γ₁[t]=e^(jΔω) ¹¹ ^(T) ^(s) ^((t−{tilde over (t)}) ¹¹ ^(−Δt) ^(o)⁾. Using the property (13) we obtainr ₁ [t]=γ ₁ [t]w ₁ ^(T) h ₁ x ₁ [t]  (15)

Similarly, for user 2 we getr ₂ [t]=e ^(jΔω) ²¹ ^(T) ^(s) ^((t−{tilde over (t)}) ²¹ ^(−Δt) ^(o) ⁾ h₂₁ [w ₁₁ x ₁ [t]+w ₂₁ x ₂ [t]]+e ^(jΔω) ²² ^(T) ^(s)^((t−{tilde over (t)}) ²² ^(−Δt) ^(o) ⁾ h ₂₂ e ^(−jΔω) ^(T) ^(T) ^(s)^((t−Δt) ^(o) ⁾ [w ₁₂ x ₁ [t]+w ₂₂ x ₂ [t]]   (16)and expanding (16)r ₂ [t]=γ ₂ [t]w ₂ ^(T) h ₂ x ₂ [t]  (17)where γ₂[t]=e^(jΔω) ²¹ ^(T) ^(s) ^((t−{tilde over (t)}) ²¹ ^(−Δt) ^(o)⁾.

Finally, the users compute the residual frequency offset and the channelestimation to demodulate the data streams x₁ [t] and x₂ [t] (4703 c).

Generalization to DIDO N×M

In this section, the previously described techniques are generalized toDIDO systems with N transmit antennas and M users.

i. Training Period for Frequency Offset Estimation

During the first training period, the signal received by the m-th useras a result of the training sequences sent from the N antennas is givenby

$\begin{matrix}{{r_{m}\lbrack t\rbrack} = {\sum\limits_{n = 1}^{N}{e^{j\;\Delta\;\omega_{mn}{T_{s}{({t - t_{mn}})}}}h_{mn}{p_{n}\lbrack t\rbrack}}}} & (18)\end{matrix}$where p_(n) is the training sequences transmitted from the n-th antenna.

After estimating the offsets Δω_(mn), ∀n=1, . . . , N, the m-th usercomputes the frequency offset between the first and the n-th transmitantenna asΔω_(T,1n)=Δω_(mn)−Δω_(m1)=ω_(T1)−ω_(Tn).  (19)Finally, the values in (19) are fed back to the base station.

ii. Training Period for Channel Estimation

During the second training period, the base station first obtains thefrequency offset feedback with the value in (19) from the m-th user orfrom the plurality of users. The value in (19) is used to pre-compensatefor the frequency offset at the transmit side. Then, the base stationsends training data to all the users for channel estimation.

For DIDO N×M systems, the signal received at the m-th user is given by

$\begin{matrix}\begin{matrix}{{r_{m}\lbrack t\rbrack} = {{e^{j\;\Delta\;\omega_{m\; 1}{T_{S}{({t - {\overset{\sim}{t}}_{m\; 1}})}}}h_{m\; 1}{p_{1}\lbrack t\rbrack}} +}} \\{\sum\limits_{n = 2}^{N}{e^{j\;{\Delta\omega}_{mn}{T_{s}{({t - {\overset{\sim}{t}}_{mn}})}}}h_{mn}e^{{- j}\;{\Delta\omega}_{T,{1\; n}}T_{s}t}{p_{n}\lbrack t\rbrack}}} \\{= {e^{j\;{\Delta\omega}_{m\; 1}{T_{s}{({t - {\overset{\sim}{t}}_{m\; 1}})}}}\left\lbrack {{h_{m\; 1}{p_{1}\lbrack t\rbrack}} + {\sum\limits_{n = 2}^{N}{e^{j{({\theta_{mn} - \theta_{m\; 1}})}}h_{mn}{p_{n}\lbrack t\rbrack}}}} \right\rbrack}} \\{= {e^{j\;{\Delta\omega}_{m\; 1}{T_{s}{({t - {\overset{\sim}{t}}_{m\; 1}})}}}{\sum\limits_{n = 1}^{N}{e^{j{({\theta_{mn} - \theta_{m\; 1}})}}h_{mn}{p_{n}\lbrack t\rbrack}}}}}\end{matrix} & (20)\end{matrix}$where θ_(mn)=−Δω_(mn)T_(s){tilde over (t)}_(mn),{tilde over(t)}_(mn)=t_(mn)+Δt and Δt is random or known delay between the firstand second transmissions of the base station. Moreover, p_(n) is thetraining sequence transmitted from the n-th antenna for frequency offsetand channel estimation.

At the receive side, the users compensate for the residual frequencyoffset by using the training sequences p_(n). Then, each users mestimates via training the vector channel

$\begin{matrix}{h_{m} = \begin{bmatrix}h_{m\; 1} \\{e^{j{({\theta_{m\; 2} - \theta_{m\; 1}})}}h_{m\; 2}} \\\vdots \\{e^{j{({\theta_{mN} - \theta_{m\; 1}})}}h_{m\; N}}\end{bmatrix}} & (21)\end{matrix}$and feeds back to the base station that computes the DIDO precoder asdescribed in the following subsection.

iii. DIDO Precoding with Pre-Compensation

The base station receives the channel state information (CSI) in (12)from the users and computes the precoding weights via blockdiagonalization (BD), such thatw _(m) ^(T) h _(i)=0,∀m≠l,m=1, . . . ,M  (22)where the vectors h_(m) are defined in (21) and w_(m)=[w_(m1), w_(m2), .. . , w_(mN)]. The base station also pre-compensates for the frequencyoffset by employing the estimate in (19) and phase offset by estimatingthe delay (Δt_(o)) between the second training transmission and thecurrent transmission. Finally, the base station sends data to the usersvia the DIDO precoder.

After this transmit processing, the signal received at user i is givenby

$\begin{matrix}\begin{matrix}{{r_{i}\lbrack t\rbrack} = {e^{j\;{\Delta\omega}_{i\; 1}{T_{s}{({t - {\overset{\sim}{t}}_{i\; 1} - {\Delta\; t_{o}}})}}}\; h_{i\; 1}{\sum\limits_{m = 1}^{M}{w_{m\; 1}{{x_{m}\lbrack t\rbrack}++}}}}} \\{\sum\limits_{n = 2}^{N}{e^{j\;{\Delta\omega}_{i\; n}{T_{s}{({t - {\overset{\sim}{t}}_{i\; n} - {\Delta\; t_{o}}})}}}h_{i\; n}e^{{- j}\;{\Delta\omega}_{T,{1\; n}}{T_{s}{({t - {\Delta\; t_{o}}})}}}{\sum\limits_{m = 1}^{M}{w_{mn}{x_{m}\lbrack t\rbrack}}}}} \\{= {{e^{j\;{\Delta\omega}_{i\; 1}{T_{s}{({t - {\Delta\; t_{o}}})}}}e^{{- \; j}\;{\Delta\omega}_{i\; 1}T_{s}{\overset{\sim}{t}}_{i\; 1}}h_{i\; 1}{\sum\limits_{m = 1}^{M}{w_{m\; 1}{x_{m}\lbrack t\rbrack}}}} +}} \\{\sum\limits_{n = 2}^{N}{e^{j\;{\Delta\omega}_{i\; 1}{T_{s}{({t - {\Delta\; t_{o}}})}}}e^{{- j}\;{\Delta\omega}_{i\; n}T_{s}{\overset{\sim}{t}}_{i\; n}}h_{\;{i\; n}}{\sum\limits_{m = 1}^{M}{w_{mn}{x_{m}\lbrack t\rbrack}}}}} \\{= {{\gamma_{i}\lbrack t\rbrack}\left\lbrack {{h_{i\; 1}{\sum\limits_{m = 1}^{M}{w_{m\; 1}{x_{m}\lbrack t\rbrack}}}} + {\sum\limits_{n = 2}^{N}{e^{j{({\theta_{i\; n} - \theta_{i\; 1}})}}h_{i\; n}{\sum\limits_{m = 1}^{M}{w_{m\; 1}{x_{m}\lbrack t\rbrack}}}}}} \right\rbrack}} \\{= {{\gamma_{i}\lbrack t\rbrack}\left\lbrack {\sum\limits_{n = 1}^{N}{e^{j{({\theta_{i\; n} - \theta_{i\; 1}})}}h_{i\; n}{\sum\limits_{m = 1}^{M}{w_{mn}{x_{m}\lbrack t\rbrack}}}}} \right\rbrack}} \\{= {{\gamma_{i}\lbrack t\rbrack}{\sum\limits_{m = 1}^{M}{\left\lbrack {\sum\limits_{n = 1}^{N}{e^{j{({\theta_{i\; n} - \theta_{i\; 1}})}}h_{i\; n}w_{mn}}} \right\rbrack{x_{m}\lbrack t\rbrack}}}}} \\{= {{\gamma_{i}\lbrack t\rbrack}{\sum\limits_{m = 1}^{m}{w_{m}^{T}h_{i}{x_{m}\lbrack t\rbrack}}}}}\end{matrix} & (23)\end{matrix}$Where γ_(i)[n]=e^(jΔω) ^(i1) ^(T) ^(s) ^((t−{tilde over (t)}) ^(i1)^(−Δt) ^(o) ⁾. Using the property (22) we obtainr _(i) [t]=γ _(i) [t]w _(i) ^(T) h _(i) x _(i) [t]  (24)

Finally, the users compute the residual frequency offset and the channelestimation to demodulate the data streams x_(i)[t].

Results

FIG. 48 shows the SER results of DIDO 2×2 systems with and withoutfrequency offset. It is possible to see that the proposed methodcompletely cancels the frequency/phase offsets yielding the same SER assystems without offsets.

Next, we evaluate the sensitivity of the proposed compensation method tofrequency offset estimation errors and/or fluctuations of the offset intime. Hence, we re-write (14) asr ₁ [t]=e ^(jΔω) ¹¹ ^(T) ^(s) ^((t−{tilde over (t)}) ¹¹ ^(−Δt) _(o) ⁾ h₁₁ [w ₁₁ x ₁ [t]+w ₂₁ x ₂ [t]]+e ^(jΔω) ¹² ^(T) ^(s)^((t−{tilde over (t)}) ¹² ^(−Δt) _(o) ⁾ h ₁₂ e ^(−j(Δω) ^(T) ^(+2Π∈)T)^(s) ^((t−Δt) ^(o) ⁾ [w ₁₂ x ₁ [t]+w ₂₂ x ₂ [t]]  (25)where ∈ indicates the estimation error and/or variation of the frequencyoffset between training and data transmission. Note that the effect of ∈is to destroy the orthogonality property in (13) such that theinterference terms in (14) and (16) are not completely pre-canceled atthe transmitter. As a results of that, the SER performance degrades forincreasing values of ∈.

FIG. 48 shows the SER performance of the frequency offset compensationmethod for different values of ∈. These results assume T_(s)=0.3 ms(i.e., signal with 3 KHz bandwidth). We observe that for ∈=0.001 Hz (orless) the SER performance is similar to the no offset case.

f. Description of One Embodiment of an Algorithm for Time and FrequencyOffset Estimation

Hereafter, we describe additional embodiments to carry out time andfrequency offset estimation (4701 b in FIG. 47). The transmit signalstructure under consideration is illustrated in H. Minn, V. K. Bhargava,and K. B. Letaief, “A robust timing and frequency synchronization forOFDM systems,” IEEE Trans. Wireless Commun., vol. 2, no. 4, pp. 822-839,July 2003, and studied in more detail in K. Shi and E. Serpedin, “Coarseframe and carrier synchronization of OFDM systems: a new metric andcomparison,” IEEE Trans. Wireless Commun., vol. 3, no. 4, pp. 1271-1284,July 2004. Generally sequences with good correlation properties are usedfor training. For example, for our system, Chu sequences are used whichare derived as described in D. Chu, “Polyphase codes with good periodiccorrelation properties (corresp.),” IEEE Trans. Inform. Theory, vol. 18,no. 4, pp. 531-532, July 1972. These sequences have an interestingproperty that they have perfect circular correlations. Let L_(cp) denotethe length of the cyclic prefix and let N_(t) denote the length of thecomponent training sequences. Let N_(t)=M_(t), where M_(t) is the lengthof the training sequence. Under these assumptions the transmitted symbolsequence for the preamble can be written as

$\begin{matrix}\left. {{s\lbrack n\rbrack} - \underset{\cdot}{\overset{\cdot}{tn}} - N_{t}} \right\rbrack & {{{for}\mspace{14mu}{n--}1},\ldots\mspace{14mu},{- L_{cp}}} \\\left. {{s\lbrack n\rbrack} = \underset{\cdot}{\overset{\cdot}{tn}}} \right\rbrack & {{{{for}\mspace{14mu} n} = 0},\ldots\mspace{14mu},{N_{t} - 1}} \\\left. {{s\lbrack n\rbrack} = {\underset{\cdot}{\overset{\cdot}{tn}} - N_{t}}} \right\rbrack & {{{{for}\mspace{14mu} n} = N_{t}},\ldots\mspace{14mu},{{2N_{t}} - 1}} \\{{s\lbrack n\rbrack} = {- {t\left\lbrack {n - {2N_{t}}} \right\rbrack}}} & {{{{for}\mspace{14mu} n} = {2N_{t}}},\ldots\mspace{14mu},{{3N_{t}} - 1}} \\\left. {{s\lbrack n\rbrack} = {\underset{\cdot}{\overset{\cdot}{tn}}\mspace{20mu} 3N_{t}}} \right\rbrack & {{{{for}\mspace{14mu} n} = {3N_{t}}},\ldots\mspace{14mu},{4N_{t}\mspace{14mu} 1.}}\end{matrix}$Note that the structure of this training signal can be extended to otherlengths but repeating the block structure. For example, to use 16training signals we consider a structure such as:└CP, B, B, −B, B, B, B, −B, B, −B, −B, B, −B, B, B, −B, B, ┘. By usingthis structure and letting N_(t)=4 M_(t) all the algorithms to bedescribed can be employed without modification. Effectively we arerepeating the training sequence. This is especially useful in caseswhere a suitable training signal may not be available.

Consider the following received signal, after matched filtering anddownsampling to the symbol rate:

${r\lbrack n\rbrack} = {{e^{2{\pi\epsilon}\; n}{\sum\limits_{l = 0}^{L}\;{{h\lbrack l\rbrack}{s\left\lbrack {n - l - \Delta} \right\rbrack}}}} + {v\lbrack n\rbrack}}$where ε is the unknown discrete-time frequency offset, Δ is the unknownframe offset, h[l] are the unknown discrete-time channel coefficients,and v[n] is additive noise. To explain the key ideas in the followingsections the presence of additive noise is ignored.

i. Coarse Frame Synchronization

The purpose of coarse frame synchronization is to solve for the unknownframe offset Δ. Let us make the following definitionsr ₁ [n]:=[r[n],r[n+1], . . . ,r[n+N _(t)−1]]^(T),r ₁ [n]:=[r[n+L _(cp) ],r[n+1], . . . ,r[n+N _(t)−1]]^(T),r ₂ [n]:=[r[n+N _(t) ],r[n+1+N _(t) ], . . . ,r[n+2N _(t)−1]]^(T),r ₂ [n]:=[r[n+L _(cp) N _(t) ],r[n+1+L _(cp) +N _(t) ], . . . ,r[n+L_(cp)+2N _(t)−1]]^(T),r ₃ [n]:=[r[n+2N _(t) ],r[n+1+2N _(t) ], . . . ,r[n+3N _(t)−1]]^(T),r ₃ [n]:=[r[n+L _(cp)+2N _(t) ], . . . ,r[n+L _(cp)+1+2N _(t) ], . . .,r[n+L _(cp)+3N _(t)−1]]^(T),r ₄ [n]:=r[n+3N _(t) ],r[n+1+3N _(t) , . . . ,r[n+4N _(t)−1]]^(T),r ₄ [n]:=[r[n+L _(cp)+3N _(t) ],r[n+L _(cp)+1+3N _(t) ], . . . ,r[n+L_(cp)+4N _(t)−1]]^(T).The proposed coarse frame synchronization algorithm is inspired from thealgorithm in K. Shi and E. Serpedin, “Coarse frame and carriersynchronization of OFDM systems: a new metric and comparison,” IEEETrans. Wireless Commun., vol. 3, no. 4, pp. 1271-1284, July 2004,derived from a maximum likelihood criterion.Method 1—Improved coarse frame synchronization: the coarse framesynchronization estimator solves the following optimization

$\hat{\Delta} = {\arg{\max\limits_{k \in z}\frac{{{P_{1}(k)}} + {{P_{2}(k)}} + {{P_{3}(k)}}}{{r_{1}}^{2} + {r_{2}}^{2} + {r_{3}}^{2} + {r_{4}}^{2} + {\frac{1}{2}\begin{pmatrix}{{{\overset{\_}{r}}_{1}}^{2} + {{\overset{\_}{r}}_{2}}^{2} +} \\{{{\overset{\_}{r}}_{3}}^{2} + {{\overset{\_}{r}}_{4}}^{2}}\end{pmatrix}}}}}$ where${P_{1}\lbrack k\rbrack} = {{{r_{1}^{*}\lbrack k\rbrack}{r_{2}\lbrack k\rbrack}} - {{r_{3}^{*}\lbrack k\rbrack}{r_{4}\lbrack k\rbrack}} - {{{\overset{\_}{r}}_{2}^{*}\lbrack k\rbrack}{{\overset{\_}{r}}_{3}\lbrack k\rbrack}}}$P₂[k] = r₂^(*)[k]r₄[k] − r₁^(*)[k]r₃[k]${P_{3}\lbrack k\rbrack} = {{{\overset{\_}{r}}_{1}^{*}\lbrack k\rbrack}{{{\overset{\_}{r}}_{4}\lbrack k\rbrack}.}}$Let the corrected signal be defined asr _(c) [n]=r[n−{circumflex over (Δ)}−┌L _(cp)/4┐].The additional correction term is used to compensate for small initialtaps in the channel and can be adjusted based on the application. Thisextra delay will be included henceforth in the channel.

ii. Fractional Frequency Offset Correction

The fractional frequency offset correction follows the coarse framesynchronization block.

Method 2—Improved fractional frequency offset correction: the fractionalfrequency offset is the solution to

${\hat{\epsilon}}_{f} = {\frac{{phase}\;{P_{1}\left\lbrack \hat{\Delta} \right\rbrack}}{2\pi\; N_{t}}.}$This is known as a fractional frequency offset because the algorithm canonly correct for offsets

${{\hat{\epsilon}}_{f}} < {\frac{1}{2\; N_{t}}.}$This problem will be solved in the next section. Let the fine frequencyoffset corrected signal be defined asr _(f) [n]=e ^(−j2π{circumflex over (∈)}) ^(f) r _(c) [n].

Note that the Methods 1 and 2 are an improvement to K. Shi and E.Serpedin, “Coarse frame and carrier synchronization of OFDM systems: anew metric and comparison,” IEEE Trans. Wireless Commun., vol. 3, no. 4,pp. 1271-1284, July 2004 that works better in frequency-selectivechannels. One specific innovation here is the use of both r and r asdescribed above. The use of r improves the prior estimator because itignores the samples that would be contaminated due to inter-symbolinterference.

iii. Integer Frequency Offset Correction

To correct for the integer frequency offset, it is necessary to write anequivalent system model for the received signal after fine frequencyoffset correction. Absorbing remaining timing errors into the channel,the received signal in the absence of noise has the following structure:

${r_{f}\lbrack n\rbrack} = {e^{j\; 2\pi\frac{nk}{N_{s}}}{\sum\limits_{l = 0}^{L_{cp}}\;{{g\lbrack l\rbrack}{s\left\lbrack {n - l} \right\rbrack}}}}$for n=0, 1, . . . , 4N_(t)−1. The integer frequency offset is k whilethe unknown equivalent channel is g[l].Method 3—Improved integer frequency offset correction: the integerfrequency offset is the solution to

$\hat{k} = {\arg{\max\limits_{{m = 0},1,\ldots\mspace{14mu},{N_{t} - 1}}{r^{*}{D\lbrack k\rbrack}{S\left( {S^{*}S} \right)}^{- 1}S^{*}{D\lbrack k\rbrack}^{*}r}}}$where r = D[k]Sg${D\lbrack k\rbrack}:={{diag}\left\{ {1,e^{j\; 2\pi\frac{n\; 1}{N_{t}}},\ldots\mspace{14mu},e^{j\; 2\pi\frac{n{({{4\; N_{t}} - 1})}}{N_{t}}}} \right\}}$$S:=\begin{bmatrix}{s\lbrack 0\rbrack} & {s\left\lbrack {- 1} \right\rbrack} & \ldots & \ldots & {s\left\lbrack {- L_{cp}} \right\rbrack} \\{s\lbrack 1\rbrack} & {s\lbrack 0\rbrack} & {s\left\lbrack {- 1} \right\rbrack} & \ldots & {s\left\lbrack {{- L_{cp}} + 1} \right\rbrack} \\{s\left\lbrack {{4\; N_{t}} - 1} \right\rbrack} & {s\left\lbrack {{4\; N_{t}} - 2} \right\rbrack} & {s\left\lbrack {{4\; N_{t}} - 3} \right\rbrack} & \ldots & {s\left\lbrack {{4\; N_{t}} - 1 - L_{cp}} \right\rbrack}\end{bmatrix}$ $g:=\begin{bmatrix}{g\lbrack 0\rbrack} \\{g\lbrack 1\rbrack} \\\vdots \\{g\left\lbrack L_{cp} \right\rbrack}\end{bmatrix}$This gives the estimate of the total frequency offset as

$\hat{\epsilon} - \frac{\hat{k}}{N_{t}} + {{\hat{\epsilon}}_{f}.}$Practically, Method 3 has rather high complexity. To reduce complexitythe following observations can be made. First of all, the productS^(S(S*S)) ⁻¹ ^(S)* can be precomputed. Unfortunately, this still leavesa rather large matrix multiplication. An alternative is to exploit theobservation that with the proposed training sequences, S*S≈I. This leadsto the following reduced complexity method.Method 4—Low-complexity improved integer frequency offset correction: alow complexity integer frequency offset estimator solves

$\hat{k} = {\arg{\max\limits_{{m = 0},1,\ldots\mspace{14mu},{N_{t} - 1}}{\left( {S^{*}D\left\lceil k \right\rceil^{*}r} \right)^{*}{\left( {S^{*}D\left\lceil k \right\rceil^{*}r} \right).}}}}$

iv. Results

In this section we compare the performance of the different proposedestimators.

First, in FIG. 50 we compare the amount of overhead required for eachmethod. Note that both of the new methods reduce the overhead requiredby 10× to 20×. To compare the performance of the different estimators,Monte Carlo experiments were performed. The setup considered is ourusual NVIS transmit waveform constructed from a linear modulation with asymbol rate of 3K symbols per second, corresponding to a passbandbandwidth of 3 kHz, and raised cosine pulse shaping. For each MonteCarlo realization, the frequency offset is generated from a uniformdistribution on [−f_(max), f_(max)].

A simulation with a small frequency offset of f_(max)=2 Hz and nointeger offset correction is illustrated in FIG. 51. It can be seen fromthis performance comparison that performance with N_(t)/M_(t)=1 isslightly degraded from the original estimator, though stillsubstantially reduces overhead. Performance with N_(t)/M_(t)=4 is muchbetter, almost 10 dB. All the curves experience a knee at low SNR pointsdue to errors in the integer offset estimation. A small error in theinteger offset can create a large frequency error and thus a large meansquared error. Integer offset correction can be turned off in smalloffsets to improve performance.

In the presence of multipath channels, the performance of frequencyoffset estimators generally degrades. Turning off the integer offsetestimator, however, reveals quite good performance in FIG. 52. Thus, inmultipath channels it is even more important to perform a robust coarsecorrection followed by an improved fine correction algorithm. Noticethat the offset performance with N_(t)/M_(t)=4 is much better in themultipath case.

Adaptive DIDO Transmission Scheme

New systems and methods for adaptive DIDO systems are described below.These systems and methods are extensions to the patent applicationsentitled “System and Method for Distributed Input-Distributed OutputWireless Communications,” Ser. Nos. 11/894,394, 11/894,362, and11/894,540, filed Aug. 20, 2007, of which the present application is acontinuation-in-part. The content of these applications has beendescribed above. The adaptive DIDO system and method described in theforegoing applications were designed to exploit instantaneous and/orstatistical channel quality information. Described below are additionaltechniques to enable adaptation between different DIDO modes assuminginstantaneous channel knowledge.

The following prior art references will be discussed below within thecontext of the embodiments of the invention. Each reference will beidentified by its corresponding bracketed number:

-   [1] K. K. Wong, R. D. Murch, and K. B. Letaief, “A joint-channel    diagonalization for multiuser MIMO antenna systems,” IEEE Trans.    Commun., vol. 2, no. 4, pp. 773-786, July 2003.-   [2] R. Chen, R. W. Heath, Jr., and J. G. Andrews, “Transmit    Selection Diversity for Unitary Precoded Multiuser Spatial    Multiplexing Systems with Linear Receivers,” IEEE Trans. on Signal    Processing, vol. 55, no. 3, pp. 1159-1171, March 2007.-   [3] R. W. Heath, Jr. and A. J. Paulraj, “Switching Between Diversity    and Multiplexing in MIMO Systems,” IEEE Trans. on Communications,    vol. 53, no. 6, pp. 962-968, June 2005.

A fundamental concept associated with link adaptation (LA) is toadaptively adjust system parameters such as modulation order, FEC codingrate and/or transmission schemes to the changing channel conditions toimprove throughput or error rate performance. These system parametersare often combined in sets of “transmission modes,” referred to hereinas DIDO modes. One embodiment of a technique for LA is to measure thechannel quality information and predict the best transmission mode basedon certain performance criterion. The channel quality consists ofstatistical channel information, as in slow LA, or (instantaneous) CSI,as in fast LA. One embodiment of the system and method described hereinis employed within the context of fast LA systems and the goal is toincrease throughput for fixed predefined target error rate.

One embodiment of a method for adaptive DIDO transmission is depicted inFIG. 21. In frequency division duplex (FDD) systems the proposedadaptive algorithm consists of the following steps: i) the users computethe channel quality indicator (CQI) 2102; ii) the users select the bestDIDO mode for transmission 2106 based on the CQI in time/frequency/spacedomains 2104; iii) the base station selects the active users andtransmits data with the selected DIDO modes via DIDO precoding. In timedivision duplex (TDD) systems, where the uplink/downlink channelreciprocity can be exploited, the base station may compute the CQIs andselect the best DIDO modes for all the users. Moreover, to compute theDIDO precoding weights, the channel state information (CSI) can becomputed at the users side in FDD systems or at the base station in TDDsystems. When the CSI is computed at the users' side and fed back to thebase station, the base station can exploit the CSI to compute the CQIfor every users to enable the adaptive DIDO algorithm.

We first define an indicator of channel quality that is used to predictthe performance of different DIDO modes and select the optimal one forgiven transmission. One example of channel quality indicator (CQI) isthe mutual information (MI) of DIDO systems defined as [1,2]

$\begin{matrix}{C = {\sum\limits_{k = 1}^{K}\;{\log_{2}{{I_{N_{k}} + {\frac{\gamma_{k}}{N_{k}}{\overset{\sim}{H}}_{k}^{H}{\overset{\sim}{H}}_{k}}}}}}} & (1)\end{matrix}$where K is the number of users, {tilde over (H)}_(k)=H_(k)T_(k) is theequivalent channel transfer matrix, H_(k) is the channel matrix for thek-th user, T_(k) is the DIDO precoding matrix for the k-th user, γ_(k)is the per-user SNR and N_(k) is the number of parallel data streamssent to user k. We observe that the CQI in (1) depends on the SNR andthe channel matrix.

The MI in (1) measures the data rate per unit bandwidth that can betransmitted reliably over the DIDO link (i.e., error-free spectralefficiency). When the spectral efficiency (SE) of given DIDO mode isbelow the MI in (1) the error rate performance is arbitrarily small,whereas when the SE exceeds (1) the error rate approaches 100%. As anexample, we plot the spectral efficiency of three DIDO modes as afunction of the MI (1) in FIG. 53. The DIDO modes consist of threeconstellation orders: 4-QAM, 16-QAM and 64-QAM. For simplicity andwithout loss of generality we assume no FEC coding. The transmitter ofthe 2×2 DIDO system employs block-diagonalization precoding scheme [1].The SE is obtained from the symbol error rate as SE=log₂M*(1−SER), whereM is the M-QAM constellation size. We simulate the channel according tothe block fading i.i.d. channel model. We generate 1000 channelrealizations and for each realization we simulate 500 AWGN samples. TheSNR values chosen for this simulation are {0, 10, 20, 30} dB.

In FIG. 53, each dot corresponds to one combination of mutualinformation and SE obtained within each AWGN block. Moreover, differentcolors are associated to different values of SNR. Similar results areexpressed in terms of SER as a function of the MI (1) in FIG. 54. Forthe case of 4-QAM, we note that when the SE exceeds MI in FIG. 53 theSER is close to 100% in FIG. 54. Unfortunately, there is a largevariance in the SER vs. MI plot that prevent the identification of thethresholds used to define the link-quality regions.

Next, we define another CQI to reduce this variance. We first expand (1)as

$C = {\sum\limits_{k = 1}^{K}{\sum\limits_{i = 1}^{N_{k}}\;{\log_{2}\left( {1 + {\frac{\gamma_{k}}{N_{k}}{\lambda_{k,i}}^{2}}} \right)}}}$where λ_(k,i) is the i-th singular value of the matrix {tilde over(H)}_(k). We observe that the per-user SER (which is a function of thepost-processing SNR) depends on

$\lambda_{\min}^{k} = {\min\limits_{{i = 1},\ldots\mspace{14mu},N_{k}}\left( \lambda_{k,i} \right)}$and the system SER is upper bounded by the user with the smallestsingular value

$\lambda_{\min} = {\min\limits_{{k = 1},\ldots\mspace{14mu},K}\left( \lambda_{\min}^{k} \right)}$among all the users [2]. Then, we define the following CQI

$\begin{matrix}{C_{\min} = {\min\limits_{{k = 1},\ldots\mspace{14mu},K}\left\{ {\min\limits_{{i = 1},\ldots\mspace{14mu},N_{k}}\left\lbrack {\log_{2}\left( {1 + {\frac{\gamma_{k}}{N_{k}}{\lambda_{k,i}}^{2}}} \right)} \right\rbrack} \right\}}} & (2)\end{matrix}$

FIG. 55 shows the SER vs. C_(min) for different DIDO modes. We observethe reduced variance compared to FIG. 54. To define the CQI thresholdsand link-quality regions, we fix the target SER. For example, if thetarget SER is 1%, the CQI thresholds are T₁=2.8 bps/Hz, T₂=5 bps/Hz andT₃=7 bps/Hz.

Finally, we compare the SER and SE performance as a function of SNR fordifferent DIDO modes against the adaptive DIDO algorithm. Results areshown in FIGS. 56 and 57. We observe that the adaptive algorithmmaintains the SER below 1% for SNR>20 dB while increasing the SE,approaching the ideal sum-rate capacity. FIGS. 58 and 59 show theperformance of the adaptive DIDO algorithm for different values of theCQI thresholds. We observe that by decreasing the CQI thresholds forfixed SNR the SE increases at the expense of larger SER. In oneembodiment, the CQI thresholds are adjusted based on the systemperformance requirements.

The proposed method for fast LA in DIDO systems includes different typesof adaptation criteria and CQIs. For example, a similar adaptive DIDOalgorithm can be designed to minimize error rate performance for fixedrate transmission, similar to the approach described in [3] for MIMOsystems. Moreover, different types of CQIs can be employed such as theminimum singular value of the composite channel matrix as

$\begin{matrix}{{\lambda_{\min} = {\min\limits_{{j = 1},\ldots\mspace{14mu},\overset{\_}{N}}\left\{ {\lambda_{j}\left( {HH}^{H} \right)} \right\}}}{where}{\overset{\_}{N} = {\sum\limits_{k = 1}^{K}\; N_{k}}}} & (3)\end{matrix}$is the total number of data streams sent to the users and H is thecomposite channel matrix obtained by stacking the channel matrices ofall the users as

$\begin{matrix}{H = \begin{bmatrix}H_{1} \\\vdots \\H_{K}\end{bmatrix}} & (4)\end{matrix}$

FIG. 60 shows the SER expressed as a function of the minimum singularvalue in (3) for 4-QAM constellation, average SNR=15 dB and single-tapchannels. The composite channel matrix in (4) is normalized such that∥H∥_(F) ²=1. We observe that, for 4-QAM constellation, the CQI thresholdto guarantee SER<1% is −16 dB. Similar results can be obtained forhigher order modulations.

The proposed method can be extended to multicarrier systems, such asorthogonal frequency division multiplexing (OFDM) systems. Inmulticarrier systems the MI in (1) and (2) is computed for eachsubcarrier and different MCSs are assigned to different subcarriers,thereby exploiting the frequency selectivity of wireless channels. Thismethod, however, may result in large number of control information toshare the CQI or DIDO mode number between transmitters and receivers. Analternative method is to group multiple subcarriers with similar channelquality and compute the average of (1) or (2) over each group ofsubcarrier. Then, different DIDO modes are assigned to different groupsof subcarriers based on the criterion described above.

III. Disclosure from U.S. Application Ser. No. 12/630,627

DIDO systems are described in the related application U.S. Pat. No.7,418,053, where multiple antennas of the same DIDO base station in FIG.2 work cooperatively to pre-cancel interference and create parallelnon-interfering data streams to multiple users. These antennas, with orwithout local transmitters and/or receivers may be spread across a widecoverage area and be interconnected to the same DIDO base station viawired or wireless links, including networks such as the Internet. Forexample, as disclosed in related U.S. Pat. No. 7,418,053 in theparagraph starting at column 6, line 31, a single base station may haveits antennas located very far apart, potentially resulting in the basestation's antenna array occupying several square kilometers. And, forexample as disclosed in related U.S. Pat. No. 7,599,420 in the paragraphstarting at column 17 line 4, and in paragraphs [0142] of U.S.application Ser. No. 11/894,362 and U.S. application Ser. No.11/894,540, the separation of antennas from a single DIDO base stationmay be physically separated by 100s of yards or even miles, potentiallyproviding diversity advantages, and the signals for each antennainstallation may either processed locally at each antenna location orbrought back to a centralized location for processing. Further, methodsfor practical deployment of DIDO systems, including addressing practicalissues associated with processing signals with widely distributed DIDOantennas, are described in the related applications U.S. Pat. No.7,599,420, U.S. application Ser. No. 11/894,362 and U.S. applicationSer. No. 11/894,540.

Recent publications [32,33] analyzed theoretically the performance ofcooperative base stations in the context of cellular systems. Inpractice, when those cooperative base stations are connected to oneanother via wireless, wired, or optical network (i.e., wide areanetwork, WAN backbone, router) to share precoded data, controlinformation and/or time/frequency synchronization information asdescribed in U.S. Pat. No. 7,418,053, U.S. Pat. No. 7,599,420, U.S.application Ser. No. 11/894,362 and U.S. application Ser. No. 11/894,540they function as multiple distributed antennas of a single DIDO basestation as shown in FIGS. 2 and 3. In the system in [32,33], however,multiple base stations (or distributed antennas of the same DIDO basestation) are constrained by their physical placements derived from cellplanning, as in conventional cellular systems.

A significant advantage of DIDO systems over prior art systems is thatDIDO systems enable the distribution of multiple cooperative distributedantennas, all using the same frequency at the same time in the same widecoverage area, without significantly restricting the physical placementof the distributed antennas. In contrast to prior art multi-usersystems, which avoid interference from multiple base transmitters at agiven user receiver, the simultaneous RF waveform transmissions frommultiple DIDO distributed antennas deliberately interfere with eachother at each user's receiver. The interference is a preciselycontrolled constructive and destructive interference of RF waveformsincident upon each receiving antenna which, rather than impairing datareception, enhances data reception. It also achieves a valuable goal: itresults in multiple simultaneous non-interfering channels to the usersvia space-time precoding techniques, increasing the aggregate throughputin a given coverage area, increasing the throughput to a given user, andsignificantly increasing the reliability and predictability ofthroughput to a given user.

Thus, when using DIDO, multiple distributed antenna RF waveformtransmission interference and user channel interference have an inverserelationship: multiple distributed antenna RF waveform interferenceresults in simultaneous non-interfering user channels.

With prior art multi-user systems, multiple base station (and/or ad hoctransceivers) RF waveform transmission interference and user channelinterference have a direct relationship: multiple base station (and/orad hoc transceivers) RF waveform interference results in simultaneousinterfering user channels.

So, what DIDO utilizes and relies upon to achieve performance far beyondprior art systems is exactly what is avoided by, and results inimpairment of, prior art systems.

And, because the number of non-interfering channels (and aggregatethroughput) grows largely proportionately with the number of DIDOdistributed antennas (unlike MU-MIMO systems, where the aggregatethroughput asymptotically levels off as the number of cluster antennasat a base station is increased), the spectrum utilization of a givencoverage area can be scaled as the number of users in an area scales,all without subdividing the coverage area by frequency or sector, andwithout requiring significant restrictions on the placement of DIDOdistributed antennas. This results in enormous efficiencies in spectrumutilization and aggregate user downlink (DL) and uplink (UL) data rates,and enormous placement flexibility for either commercial or consumerbase station installation.

In this way, DIDO opens the door to a very large increase in multi-userwireless spectrum efficiency by specifically doing exactly what priorart systems had been meticulously designed to avoid doing.

As illustrated in FIGS. 61-62, in one embodiment, DIDO systems consistof:

DIDO Clients 6110: wireless devices that estimate the channel stateinformation (CSI), feedback the CSI to the transmitters and demodulateprecoded data. Typically each user would have a DIDO client device.

DIDO Distributed Antennas 6113: wireless devices interconnected via anetwork that transmit precoded data to all DIDO clients. A wide varietyof network types can be used to interconnect the distributed antennas6113 including, but not limited to, a local area network (LAN), a wirearea network (WAN), the Internet, a commercial fiber optic loop, awireless network, or any combination thereof. In one embodiment, toprovide a simultaneous independent channel to each client, the number ofDIDO distributed antennas is at least equal to the number of clientsthat are served via precoding, and thereby avoids sharing channels amongclients. More DIDO distributed antennas than clients can be used toimprove link reliability via transmit diversity techniques, or can beused in combination with multi-antenna clients to increase data rateand/or improve link reliability. Note that “distributed antenna”, asused herein, may not be merely an antenna, but refers to a devicecapable of transmitting and/or receiving through at least one antenna.For example, the device may incorporate the network interface to theDIDO BTS 6112 (described below) and a transceiver, as well as an antennaattached to the transceiver. The distributed antennas 6113 are theantennas that the DIDO BTS 6112, utilizes to implement the DIDOmulti-user system.

DIDO Base Transceiver Station (“BTS” or “base station”) 6112: computesthe precoding weights based on the CSI obtained from all users in a DIDOsystem and sends precoded data to the DIDO distributed antennas. The BTSmay be connected to the Internet, public switched telephone network(PSTN) or private networks to provide connectivity between users andsuch networks. For example, upon clients' requests to access webcontent, the CP fetches data through the Internet and transmits data tothe clients via the DIDO distributed antennas.

DIDO Base Station Network (BSN) 6111: One embodiment of DIDO technologyenables precisely controlled cooperation among multiple DIDO distributedantennas spread over wide areas and interconnected by a network. In oneembodiment, the network used to interconnect the DIDO distributedantennas is a metro fiber optic ring (preferably, with the DIDOdistributed antennas connecting to the metro fiber optic ring atlocations where it is convenient), characterized by relatively lowlatency and reasonably high throughput (e.g. throughput to each DIDOantenna comparable to the wireless throughput achievable from that DIDOantenna). The fiber optic ring is used to share control information andprecoded data among different stations. Note that many othercommunication networks can be used instead of a metro fiber optic ring,including fiber optic networks in different topologies other than aring, fiber-to-the-home (FFTH), Digital Subscriber Lines (DSL), cablemodems, wireless links, data over power line, Ethernet, etc. Thecommunication network interconnecting the DIDO distributed antennas maywell be made up of a combination of different network technologies. Forexample, some DIDO distributed antennas may be connected to DSL, some tofiber, some to cable modems, some on Ethernet, etc. The network may be aprivate network, the Internet, or a combination. Thus, much like priorart consumer and commercial WiFi base stations are connected via avariety of network technologies, as is convenient at each location, somay be the DIDO distributed antennas. Whatever form this network takes,be it a uniform technology, or a variety of technologies, it is referredherein as the Base Station Network or “BSN.” In one embodiment of theBSN, there is an approximate 10-30 msec round trip time (RTT) latencybetween BTS and the DIDO distributed antennas, due to the packetswitched nature of existing fiber or DSL networks. The variance of thatlatency (i.e., jitter) is of the order of milliseconds. If lower latency(i.e., <1 msec) and jitter is required for DIDO systems, the BSN may bedesigned with dedicated fiber links. Depending on the quality of serviceoffered to different DIDO clients, a combination of low and high latencyBSNs can be employed.

Depending on the layout of the network interconnecting the DIDOdistributed antennas 6113, one or multiple DIDO BTSs can be used in agiven coverage area. We define a DIDO cell as the coverage area servedby one DIDO BTS. One embodiment with circular topology is depicted inFIG. 61 (the dots are the DIDO clients 6110, and crosses are the DIDOdistributed antennas 6113). In more realistic scenarios the BSN does nothave circular shape as in FIG. 61. In fact, the DIDO distributedantennas may be placed randomly within the DIDO cell, whereverconnections to the BSN are available and/or conveniently reached, asdepicted in FIG. 62. If the coverage area is one city, in one embodimentmultiple DIDO cells (associated to multiple DIDO BTSs) can be designedto cover the whole city. In that case, cellular planning is required toallocate different frequency channels to adjacent DIDO cells to avoidinter-cell interference. Alternatively, one DIDO cell can be designed tocover the entire city at the expense of higher computational complexityat the DIDO BTS (e.g., more CSI data from all the users in the same DIDOcell to be processed by the BTS) and larger throughput requirement overthe network interconnecting the DIDO distributed antennas.

In one embodiment of the invention, the BSN 6111 is used to deliverprecoded baseband data from the BTS 6112 to the DIDO distributedantennas 6113. As shown in FIG. 63, the DIDO distributed antenna 6313includes a radio transceiver 6330 equipped with digital-to-analogconverter (DAC), analog-to-digital converter (ADC), mixer and coupled to(or including) a power amplifier 6338. Each DIDO distributed antennareceives the baseband precoded data 6332 over the BSN 6311 (such asfiber optic cable 6331) from the BTS 6312, modulates the signal at thecarrier frequency and transmits the modulated signal to the clients overthe wireless link via antenna 6339. As illustrated in FIG. 63, areference clock signal is provided to the radio transceiver by areference clock generator 6333.

In another embodiment of the invention, the BSN is used to carrymodulated signals as illustrated in FIG. 64, which shows the structureof DIDO systems employing RF-over-fiber. For example, if the BSN is afiber optic channel 6431 with sufficient bandwidth, a radio frequency(RF) modulated signal is sent over the fiber according to a system suchas that described in [17,18]. Multiple radios 6440 (up to as many as thenumber of DIDO distributed antennas) can be employed at the BTS 6412 tomodulate the baseband signals carrying precoded data. The RF modulatedsignal is converted into optical signal by the radio interface unit(RIU) 6441. One example of an RIU for UHF is the FORAX LOS1 by Syntonics[19]. The optical signal propagates from the BTS to the DIDO distributedantennas 6413 over the BSN 6411. The DIDO distributed antennas areequipped with one amplifier interface unit (AIU) 6445 that converts theoptical signal to RF. The RF signal is amplified by amplifier 6448 andsent through the antenna 6449 over the wireless link. An advantage ofDIDO with RF-over-fiber solution is significant reduction in complexityand cost of the DIDO distributed antennas. In fact, the DIDO distributedantenna consists only of one AIU 6445, power amplifier 6448 and antenna6449. Moreover, if the fiber propagation delay is known and fixed, allthe radios at the BTS can be locked to the same reference clock 6442 asin FIG. 64, with an appropriate delay to compensate for the propagationdelay, and no time/frequency synchronization is required at the DIDOdistributed antenna, thereby simplifying further the complexity of DIDOsystems.

In another embodiment, existing cellular towers with antennas,transceivers, and backhaul connectivity are reconfigured such that thebackhauls are connected to a DIDO BTS 6112. The backhaul connectivitybecomes functionally equivalent to the BSN 6111. Then, as describedpreviously, the cellular transceivers and antennas become functionallyequivalent to the DIDO distributed antennas 6113. Depending on thetransceivers and antennas installed in existing cellular phone towers,they may need to be reconfigured or replaced, so as to be able tooperate in a DIDO configuration. For example, the transmitters may havebeen configured to transmit at a low power level so as to not causeinterference with a nearby cell using the same frequency. With DIDO,there is no need to mitigate such waveform interference, and indeed,such waveform interference increases the spectrum utilization of thecoverage area beyond that achievable in a prior art cellularconfiguration.

In another embodiment, existing cellular towers are partially used forDIDO, as described in the preceding paragraph, and partially used asconventional cellular towers, so as to support compatibility withexisting cellular devices. Such a combined system can be implemented ina number of different ways. In one embodiment, TDMA is used to alternatebetween DIDO use and conventional cellular use. So, at any given time,the cellular towers are used for only DIDO or for conventional cellularcommunications.

Some key features and benefits of DIDO systems, compared to typicalmulti-user wireless systems, including cellular systems employingMU-MIMO techniques, are:

Large spatial diversity: Because DIDO distributed antennas can belocated anywhere within a coverage area, and work cooperatively withoutchannel interference, this results in larger transmit antenna spacingand multipath angular spread. Thus, far more antennas can be used, whilestill maintaining spatial diversity. Unlike prior art commercial orconsumer base stations, DIDO distributed antennas can be placed anywherethere is a reasonably fast Internet (or other network) connection, evenif it is only a few feet from the ground, indoor or outdoor. Reducedcoverage (e.g., due to lower transmit antenna height or physicalobstacles) can be compensated by larger transmit power (e.g., 100 Wrather than ˜200 mW as in typical cellular systems in urban areas or˜250 mW in typical WiFi access points) because there is no concern (orfar less concern than with prior art cellular systems) abouthigher-powered transmissions interfering with another cell or WiFiaccess point using the same frequency. Larger spatial diversitytranslates into a larger number of non-interfering channels that can becreated to multiple users. Theoretically (e.g., due to large antennaspacing and angular spread), the number of spatial channels is equal tothe number of transmit DIDO stations. That yields an n× improvement inaggregate DL data rate, where n is the number of DIDO stations. Forexample, whereas prior art cellular system might achieve a maximum ofnet 3× improvement in aggregate spectrum utilization, a DIDO systemmight achieve a 10×, 100× or even greater improvement in aggregatespectrum utilization.

Uniform rate distribution: Since the DIDO distributed antennas can bedispersed throughout a wide area, far more users can be characterized bygood signal-to-noise ratio (SNR) from one or more DIDO distributedantennas. Then, far more users can experience similar data rates, unlikecellular systems where cell-edge users suffer from poor link-budget andlow data rate.

Cost effective: DIDO distributed antennas can be designed as inexpensivedevices with single antenna transceivers (similar to WiFi accesspoints). Moreover, they do not require costly real estate or expensiveinstallation as cell towers because of the ability to flexibly locatethem within the coverage area.

2. Methods for Implementation and Deployment of DIDO systems

The following describes different embodiments of practical deployment ofDIDO systems.

a. Downlink Channel

The general algorithm used in one embodiment to enable DIDOcommunications over wireless links is described as follows.

CSI Computation: All DIDO clients compute the CSI from all DIDOdistributed antenna transmitters based on training sequences receivedfrom DIDO distributed antennas as shown in FIG. 4. The CSI is fed backwirelessly from DIDO clients to DIDO distributed antennas 6113 via TDMAor MIMO techniques as described in the related applications and in FIG.5, and then the DIDO distributed antennas 6113 send the CSI via the DIDOBSN 6111 to the DIDO BTS 6112.

Precoding Computation: the DIDO BTS 6112 computes the precoding weightsfrom the CSI feedback from the entire DIDO cell. Precoded data are sentfrom the DIDO BTS 6112 to the DIDO distributed antennas in FIG. 6 viathe DIDO BSN 6111. One precoded data stream is sent to each of the DIDOdistributed antennas.

Precoded Data Transmission: the DIDO distributed antennas transmitprecoded data to all clients over the wireless links.

Demodulation: the DIDO clients demodulate the precoded data streams.

In DIDO systems, the feedback loop in FIGS. 19-20 consists of:transmission of the training sequence for channel estimation from DIDOdistributed antennas to clients; CSI estimation by clients; CSI feedbackfrom clients via the DIDO distributed antennas through the DIDO BSN 6111to the DIDO BTS 6112; precoded data transmission from DIDO BTS 6112through the DIDO BSN 6111 to DIDO distributed antennas to clients. Toguarantee the CSI is up-to-date for successful DIDO precoding and datademodulation at the client side, the delay over the feedback loop shouldbe lower than the channel coherence time. The feedback loop delaydepends on the BTS computational resources relative to the computationalcomplexity of the DIDO precoding as well as latency over the BSN.Processing at each client and DIDO distributed antenna is typically verylimited (i.e., on the order of a microsecond or less with a single DSPor CPU), depending on the hardware and processor speed. Most of thefeedback loop delay is due the latency for transmission of precoded datafrom the DIDO BTS 6112 to the DIDO distributed antennas 6113 over theDIDO BSN 6111 (e.g., on the order of milliseconds).

As discussed above, a low latency or high latency BSN can be used inDIDO systems depending on the available network. In one embodiment, theDIDO BTS 6112 switches among two or more types of BSN networkinfrastructure based on the each users' channel coherence time. Forexample, outdoor clients are typically characterized by more severeDoppler effects due to the potential of fast mobility of clients orobjects within the channel (i.e., resulting in low channel coherencetime). Indoor clients have generally fixed wireless or low mobilitylinks (e.g., high channel coherence time). In one embodiment, DIDOdistributed antennas connected to low latency BSN network infrastructure(e.g., dedicated fiber rings) are assigned to outdoor clients, whereasDIDO distributed antennas connected to high latency BSN networkinfrastructure (e.g., consumer Internet connections such as DSL or cablemodems) are assigned to serve indoor clients. To avoid interferenceamong transmissions to the different types of clients, indoor andoutdoor clients can be multiplexed via TDMA, FDMA or CDMA schemes.

Moreover, DIDO distributed antennas connected to low latency BSNs canalso be used for delay-sensitive algorithms such as those used forclient time and frequency synchronization.

We observe that DIDO provides an inherently secure network when morethan one DIDO distributed antenna is used to reach a user. In fact, theprecoded streams from the BTS to the DIDO distributed antennas consistof linear combinations of data (for different clients) and DIDOprecoding weights. Then, the data stream sent from the BTS to the BSNgenerally cannot be demodulated at the DIDO distributed antenna, sincethe DIDO distributed antenna is unaware of the precoding weights used bythe BTS. Also, the precoding weights change over time as the complexgain of the wireless channels from DIDO distributed antenna-to-clientvaries (due to Doppler effects), adding an additional level of security.Moreover, the data stream intended to each client can be demodulatedonly at the client's location, where the precoded signals from alltransmit DIDO distributed antennas recombine to provide userinterference-free data. At any other location, demodulation of dataintended to one particular user is not possible due to high levels ofinter-user interference.

b. Uplink Channel

In the uplink (UL) channel, the clients send data (e.g., to request Webcontent to the DIDO BTS 6112 from the Internet), CSI and controlinformation (e.g., time/frequency synchronization, channel qualityinformation, modulation scheme, etc.). In one embodiment, there are twoalternatives for the UL channel that may be used separately or incombination: i) clients communicate directly to the DIDO BTS 6112 viaTDMA, FDMA or CDMA schemes; ii) clients communicate to multiple DIDOdistributed antennas by creating spatial channels via MIMO techniques asin FIG. 7 (in the MIMO case, however, transmission time synchronizationamong clients is required).

c. Time and Frequency Synchronization

In one embodiment, the DIDO distributed antennas are synchronized intime and frequency. If RF-over-fiber is employed as in FIG. 64, allradio transceivers at the BTS are locked to the same reference clock6442, thereby guaranteeing perfect time and frequency synchronization.Assuming negligible jitter over the DIDO BSN 6111, artificial delays canbe added to the transmit RF waveforms at the DIDO BTS 6112 side tocompensate for propagation delays over the DIDO BSN 6111 to differentDIDO distributed antennas.

If the DIDO BSN 6111 is used to carry baseband waveforms as in FIG. 63,time and frequency synchronization is required for the radiotransceivers at different DIDO distributed antennas. There are variousmethods to achieve this synchronization, and more than one method can beused at once.

i. Time and Frequency Synchronization Via GPSDO

In one embodiment time/frequency synchronization is achieved byconnecting the transmitter in radio transceiver 6330 to a GPSDisciplined Oscillators (GPSDO). A crystal clock with high frequencystability and low jitter (e.g., Oven-Controlled Crystal Oscillator,OCXO) is used in one embodiment.

ii. Time and Frequency Synchronization Via Power Line Reference

An alternate embodiment utilizes the 60 Hz (in the United States, 50 Hzin other regions) signal available over power lines as a common clockreference for all transmitters. Based on empirical measurements, thejitter of the 60 Hz reference signal (after low pass filtering) can beon the order of 100 nanoseconds. It would be necessary, however, tocompensate for deterministic offsets due to variable propagation pathlength along the power lines at different locations.

iii. Time and Frequency Synchronization with Free-Running Clocks

An alternative embodiment is used to compensate the time and frequencyoffsets across different DIDO distributed antennas whose clocks are notsynchronized to an external clock reference, but rather are free-runningas described in the related U.S. Pat. No. 7,599,420 and in FIGS. 45, 46and 47.

Coarse Time Synchronization: In one embodiment, all DIDO distributedantennas have free-running clocks as illustrated in FIG. 46 that cangenerate a periodic reference signal (one pulse per second (PPS) in oneembodiment). The DIDO BTS 6112 sends an initial trigger signal to allDIDO distributed antennas via the DIDO BSN 6111 to trigger theirtransmission at the next PPS. The roundtrip time (RU) over the BSN isassumed to be of the order of particular time interval (10 msec in oneembodiment, or ˜5 ms in each direction), so all DIDO distributedantennas will start transmitting with a relative time offset of at most1 sec+5 msec. Each DIDO distributed antenna sends one training signal(i.e., Zadoff-Chu sequence or methods for GPS systems in [6]) to allusers for initial time offset estimation. Alternatively, only a subsetof users (those with highest SNR) can be selected to reduce thecomplexity of the algorithm. Training signals from different DIDOdistributed antennas are orthogonal or sent via TDMA/FDMA to avoidinterference. The users estimate the relative time of arrival from everytransmitter by correlating the receive signal with the known trainingsequence. The same training sequence can be sent periodically and thecorrelation can be averaged over a long period of time (e.g., on theorder of minutes in one embodiment) to average-out multipath effects,particularly in the case of mobile users.

In one embodiment of the invention, time-reversal techniques [31] can beapplied to pre-compensate for multipath effects at the transmitter andobtain precise time of arrival estimates. Then, the users compute thedelays (i.e., deterministic time offsets) of each transmitter relativeto a given time reference (e.g., one of the DIDO distributed antennascan be chosen as an absolute time reference). The relative time offsetis fed back from the clients to the DIDO distributed antennas ordirectly to the DIDO BTS 6112. Then, each DIDO antenna averages the timeoffset information obtained from all the users and adjusts its PPS (andclock reference) according to that.

In one embodiment, the time offset is computed from measurements by manyusers to average out the difference in propagation delay across users.For example, FIG. 65 shows one case with two DIDO distributed antennas6551 and 6552 perfectly synchronized (e.g., via GPSDO) and two users6553 and 6554 with Line Of Sight (LOS) channels. We use TX1 6551 as theabsolute time reference. Since we assume the transmitters are perfectlysynchronized, the average time offset between users should be zero.However, if we average the offset information only across two users, asin FIG. 65, the average offset of TX2 6552 relative to TX1 6551 would be(7+(−2))/2=2.5 usec. By relying on the Monte Carlo method, we canaverage out this effect as the number of users increases. It is possibleto simulate the bias of this algorithm depending on the TX/RXdistribution and channel delay spread.

Fine Time Synchronization: Once the coarse time offset is removed, DIDOdistributed antennas can keep running the algorithm periodically toimprove the offset estimates. Moreover, the DIDO transmit stations aretypically at fixed locations (e.g. transceiver DIDO distributed antennasconnected to the DIDO BSN 6111). Hence the algorithm should convergeafter a period of time. The same algorithm is rerun every time one DIDOdistributed antenna changes its location or a new DIDO distributedantenna is added to the DIDO BSN 6111.

Frequency Offset Compensation: once the 1 PPS reference signals at allDIDO distributed antennas are synchronized, the DIDO distributedantennas send training to one or multiple users to estimate the relativefrequency offset between stations. Then, the frequency offsetcompensation method described in the related U.S. Pat. No. 7,599,420 andFIG. 47 is applied to transmit precoded data to all users whilecompensating for the offset. Note that for the best performance of thisalgorithm, two conditions need to be satisfied: i) good SNR between allDIDO transmitters and the user (or users) responsible for frequencyoffset estimation; ii) good clock stability: if the OCXOs at the DIDOdistributed antennas are stable, the frequency offset estimation can becarried out only occasionally, thereby reducing the feedbackinformation.

d. Control Channel Via the BSN

In one embodiment, the DIDO BSN 6111 is used for at least the followingthree purposes:

CSI Feedback: The DIDO clients feedback the CSI wirelessly to the DIDOdistributed antennas. If TDMA, FDMA or CDMA schemes are used forfeedback, only one DIDO distributed antenna (the one with best SNR toall users) is selected to receive the CSI. If MIMO techniques areemployed, all DIDO distributed antennas are used simultaneously todemodulate the CSI from all clients. Then the CSI is fed back from theDIDO distributed antennas to the DIDO BTS 6112 via the DIDO BSN 6111.Alternatively, the CSI can be fed back wirelessly directly from theclients (or the DIDO distributed antennas) to a DIDO BTS 6112 equippedwith one antenna via TDMA or CDMA schemes. This second solution has theadvantage of avoiding latency caused by the DIDO BSN 6111, but may notbe achievable if the wireless link between each of the clients (or theDIDO distributed antennas) and the DIDO BTS 6112 is not of high enoughSNR and reliability. To reduce the throughput requirement over the ULchannel, the CSI may be quantized or any number of limited feedbackalgorithms known in the art can be applied [28-30].

Control Information: The DIDO BTS 6112 sends control information to theDIDO distributed antennas via the DIDO BSN 6111. Examples of controlinformation are: transmit power for different DIDO distributed antennas(to enable power control algorithms); active DIDO distributed antennaIDs (to enable antenna selection algorithms); trigger signals for timesynchronization and frequency offset values.

Precoded data: the DIDO BTS 6112 sends precoded data to all DIDOdistributed antennas via the DIDO BSN 6111. That precoded data is thensent from the DIDO distributed antennas synchronously to all clientsover wireless links.

Case Study 1: DIDO in UHF Spectrum

a. UHF and Microwave Spectrum Allocation

Different frequency bands are available in the United States as possiblecandidates for DIDO system deployment: (i) the unused televisionfrequency band between 54-698 MHz (TV Channels 2-51 with 6 MHz channelbandwidth), recommended by the White Spaces Coalition to deliver highspeed Internet services; (ii) the 734-746 MHz and 746-756 MHz planned tobe used for future developments of LTE systems by AT&T and Verizon,respectively; (iii) the 2.5 GHz band for broadband radio service (BRS),consisting of 67.5 MHz of spectrum split in five channels for futuredeployment of WiMAX systems.

b. Propagation Channel in UHF Spectrum

We begin by computing the path loss of DIDO systems in urbanenvironments at different frequencies allocated for White Spaces. We usethe Hata-Okumura model described in [7], with transmit and receiveantenna heights of 1.5 meter (e.g., indoor installation of the DIDOdistributed antennas) and 100 W transmit power. To determine the range,we use −90 dBm target receive sensitivity of typical wireless devices.FIG. 66 shows the path loss at 85 MHz and 400 MHz. In one embodiment,the expected range for DIDO systems is between 1 Km and 3 Km dependingon the frequency.

Some prior art multi-user systems proposed for White Spaces have similarinterference avoidance protocols as WiFi, although at UHF frequencies.We compare DIDO UHF results against the path loss for WiFi systems with250 mW transmit power. The range for WiFi extends only between 60 meters(indoor) and 200 meters (outdoor). Wider range achievable by DIDOsystems is due to larger transmit power and lower carrier frequency(subject to generally lower attenuation from obstacles at UHFfrequencies). But, we observe that WiFi systems were deliberatelylimited in power because large transmit power would create harmfulinterference to other users using WiFi systems (or other users in the2.4 GHz ISM spectrum) because only one interfering access point can betransmitting at once, and by extending the range, increasingly more WiFiaccess points would interfere with one another. Contrarily, in DIDOsystems inter-user interference is suppressed by multiple DIDOdistributed antennas transmitting precoded data to the clients.

Next, we summarize the parameters that characterize time, frequency andspace selectivity in UHF channels.

Time selectivity is caused by relative motion of transmitter andreceiver that yields shift in the frequency domain of the receivedwaveform, known as the Doppler effect. We model the Doppler spectrumaccording to the well known Jakes' model for rich scatteringenvironments (e.g., urban areas), and compute the channel coherence timefrom the maximum Doppler shift according to [14]. As a rule of thumb,the channel complex gain can be considered constant over a period oftime corresponding to one tenth of the channel coherence time(Δt=T_(C)/10). FIG. 67 shows the period Δt as a function of the relativevelocity between transmitter and receiver for different frequencies inthe UHF band.

In DIDO systems, Δt provides the constraint to the maximum delay thatcan be tolerated between estimation of the channel state information(CSI) and data transmission via DIDO precoding. For example, if theconstraint is Δt=10 msec, the maximum speed that can be tolerated byDIDO systems is 4 mph at 700 MHz, 7 mph at 400 MHz, and 57 mph at 50MHz. If a low latency network is used for the BSN and the DIDO BTS 6112is in the vicinity of the DIDO distributed antennas (so as to minimizenetwork transit delay), far less than 10 msec RTT can be achieved Δt.For example, if Δt=1 msec, at 400 MHz, DIDO can tolerate approximatelyhighway speeds of 70 Mph.

Frequency selectivity depends on the channel delay spread. Typicalvalues of delay spread for indoor environments are below 300 nsec[8-10]. In urban and suburban areas the delay spread ranges between 1and 10 usec [11,12]. In rural environments it is typically on the orderof 10 to 30 usec [11-13].

Space selectivity depends on the channel angular spread and antennaspacing at transmit/receive side. In urban environments, the channelangular spread is typically large due to rich scattering effects. Inrich scattering environments, it was shown that the minimum antennaspacing (either at transmitter or receiver sides) to guarantee goodspatial selectivity is about one wavelength [15,16].

In FIG. 68 we summarize the main propagation effects in DIDO systems forthree different carrier frequencies. We observe that lower frequenciesprovide better range and robustness to mobile speed at the expense oflarger antenna size and distance between transceivers. A good tradeoffis offered by the 400 MHz band. This band can support pedestrian speedat a ˜10 msec limitation to transmit control information from thecentralized processor to the DIDO distributed antennas over theInternet, and it can support highway speeds with a ˜1 msec limitation.

c. Practical Implementation of DIDO Systems in UHF Spectrum

Based on the channel parameters and systems constraints described above,we provide one embodiment of DIDO system design in UHF spectrum asfollows:

Bandwidth: 5 to 10 MHz, depending on UHF spectrum availability.

Carrier frequency: 400 MHz for best tradeoff between range/Doppler andantenna size/spacing.

Modulation: orthogonal frequency division multiplexing (OFDM) is used toreduce receiver complexity and exploit channel frequency diversity (viainterleaving) as in FIG. 11. The cyclic prefix is 10 usec, based uponthe maximum delay spread expected in UHF channels, corresponding to 50channel taps at 5 MHz bandwidth. The OFDM waveform can be designed with1024 tones, corresponding to ˜5% loss in spectral efficiency. The totalOFDM symbol length (including cyclic prefix and data) is 215 usec.

Packet Size: is limited by the latency over the DIDO BSN 6111 andDoppler effects. For example, the nominal RTT of one embodiment is 10msec. Then, the time required to send precoded data from the DIDO BST6112 to the DIDO distributed antennas is ˜5 msec (half RTT). Assumingmaximum users' speed of 7 mph at 400 MHz as in FIG. 68, the channel gaincan be considered constant for approximately 10 msec. Hence, we use theremaining 5 msec to send data and define the packet size as(5e-3/215e-6)≈23 OFDM symbols. Note that higher users' speeds yield alarger Doppler effect resulting in a lower number of OFDM symbols sentper packet, unless the latency over the DIDO BSN 6111 can be reduced.

CSI Estimation and Precoding: With the system parameters above, trainingfor CSI estimation is sent every 5 msec. The users estimate/feedback theCSI and ˜5 msec later they receive 5 msec of precoded data todemodulate.

DIDO Distributed Antenna Placement Within the Coverage Area: AlthoughDIDO distributed antennas can be placed on existing cell towers, as apractical matter, given limited real estate available at existing celltowers, there may be a limited number of antenna locations available.For example, if a maximum of four antennas were placed on each towerthis might yield up to 3× increase in data rate as shown in [4] (due tolack of spatial diversity). In this configuration, latency across DIDOtransmitters is negligible, since they are all placed on the same tower,but without additional spatial diversity, the gain in spectralutilization will be limited. In one embodiment, the DIDO distributedantennas are placed in random locations throughout the coverage area allconnected to the DIDO BSN 6111. Unlike a the coverage area of given cellin a prior art cellular system, which is based on transmission rangefrom the cell tower, the coverage area of a DIDO cell is based insteadon the transmission range of each DIDO distributed antenna, which inaccordance with the path loss model in one embodiment is approximately 1Km. Thus, a user within 1 Km of at least one DIDO distributed antennawill receive service, and a user within range of several DIDOdistributed antennas will get non-interfering service from the DIDOdistributed antennas within range.

Case Study 2: DIDO in NVIS Links

Another application of DIDO technology is in the HF band. The keyadvantage of HF systems is extended coverage in the 1-30 MHz frequencyband due to reflection off of the ionosphere. One example of propagationvia the ionosphere is near-vertical incident skywave (NVIS) wheresignals sent towards the sky with high elevation angles from the horizonbounce off the ionosphere and return back to Earth. NVIS offersunprecedented coverage over conventional terrestrial wireless systems:NVIS links extend between 20 and 300 miles, whereas typical range ofterrestrial systems is between 1 and 5 miles.

Hereafter, we present the characteristics of NVIS links based on resultsobtained from the literature and our experimental data. Then we presenta practical implementation of DIDO systems in NVIS links that weredescribed in the related U.S. Pat. No. 7,418,053, U.S. Pat. No.7,599,420, U.S. application Ser. No. 11/894,362, U.S. application Ser.No. 11/894,394 U.S. application Ser. No. 11/143,503 and U.S. applicationSer. No. 11/894,540 and in FIG. 10.

a. HF Spectrum Allocation

The HF band is divided into several subbands dedicated to differenttypes of services. For example, the Maritime band is defined between 4MHz and 4.438 MHz. According to the Federal Communications Commission(FCC) licensing database (i.e., universal licensing systems, “ULS”),there are 1,070 licenses authorized to operate in this Maritime band.There are 146 channels of 3 KHz bandwidth each, covering 0.438 MHzbandwidth. Most of the transceiver stations operating in the Maritimeband are located along the coast of the US territory as depicted in FIG.69. Hence, DIDO-NVIS distributed antennas operating inland (far awayfrom the coast) would not cause harmful interference to those Maritimestations or vessels at sea. Moreover, along the coast, cognitive radiotechniques can be applied to detect channels in use and avoidtransmission over DIDO-NVIS links in those channels. For example, if theDIDO-NVIS system is designed to transmit broadband OFDM waveforms (˜1MHz bandwidth), the OFDM tones corresponding to active channels in theMaritime band can be suppressed to avoid interference.

Other portions of the HF spectrum are occupied by the Aeronautical bandwithin [3.3,155] MHz and [3.4,3.5] MHz, and the Amateur radio bandsdefined in the ranges [1.8,2] MHz, [3.5,4] MHz, [5.3305,5.4035] MHz,[7,7.3] MHz, [10.10,10.15] MHz, [14,14.35] MHz, [18.068,18.168] MHz,[21,21.450] MHz, [24.89,24.99] MHz, [28,29.7] MHz. Our experimentalmeasurements have shown that the Amateur radio band is mostlyunutilized, particularly during daytime, allowing DIDO-NVIS linkswithout causing harmful interference. Moreover, similarly to theMaritime band, coexistence of DIDO-NVIS systems with Amateur radiotransceivers may be enabled by cognitive radio techniques.

b. NVIS Propagation Channel

We provide an overview of radio wave propagation through the ionosphere.Then we describe path loss, noise and time/frequency/space selectivityin typical NVIS channels.

The ionosphere consists of ionized gas or plasma. The plasma behaves asan electromagnetic shield for radio waves propagating from Earth upwardsthat are refracted and reflected back to Earth as in FIG. 10. Thestronger the level of ionization, the higher the critical frequency ofthe plasma and number of reflections in the ionosphere, resulting inimproved signal quality over NVIS links. The ionization level depends onthe intensity of solar radiations that strike the ionosphere producingplasma. One empirical measure of the solar activity is the sunspotnumber (SSN) that varies on 11-year cycles as shown in FIG. 70. Hence,the performance of DIDO-NVIS systems is expected to vary throughoutevery 11-year cycle, yielding highest SNR and largest number of usableHF bands at the peak of the cycle.

Due to the absence of obstacles in NVIS links, the propagation loss ismostly due to free space path loss (i.e., Friis formula), withoutadditional attenuation factors as in standard terrestrial wirelesssystems. Depending on the time of the day and incident angle to theionosphere, propagating waveforms may suffer from additional 10-25 dBloss due to attenuation from the D layer (i.e., lowest layer of theionosphere). FIG. 71 compares the path loss in NVIS links against nextgeneration wireless systems such as WiMAX and 3GPP long term evolution(LTE) in macrocells with 43 dBm transmit power. For WiMAX and LTE weused 2.5 GHz and 700 MHz carrier frequencies, respectively. NVIS linksyield better signal quality (i.e., wider coverage) than standard systemsfor distances greater than ˜1 mile.

Any wireless system is affected by thermal noise produced internally toradio receivers. In contrast to standard wireless systems, HF links areseverely affected by other external noise sources such as: atmosphericnoise, man-made noise and galactic noise. Man-made noise is due toenvironmental sources such as power lines, machinery, ignition systems,and is the main source of noise in the HF band. Its typical values rangebetween −133 and −110 dBm/Hz depending on the environment (i.e., remoteversus industrial).

From our Doppler measurements, we observed typical channel coherencetime in NVIS links is of the order of seconds, That is about 100 timeslarger than the Δt=10 msec constraint on the DIDO feedback loop over theDIDO BSN 6111. Hence, in DIDO-NVIS systems a long feedback delay overthe DIDO BSN 6111 can be tolerated due to extremely high channelcoherence time. Note that our measurements assumed fixed wireless links.In case of mobile stations, the channel coherence time is expected to beof the order of 2 sec in a very high speed scenario (i.e., vehicle orairplane moving at 200 mph) that is still orders of magnitude higherthan the latency over the DIDO BSN 6111.

Typical values of delay spreads in NVIS channels are around 2 mscorresponding, corresponding to the roundtrip propagation delayEarth-ionosphere (about 300 Km high). That value may be larger (˜5 msec)in presence of multilayer refractions in the ionosphere.

The angular spread in NVIS links is typically very small (less than 1degree, based on our measurements and simulations). Hence, large antennaspacing is required to obtain spatially selective channels and exploitspatial diversity via DIDO techniques. Strangeways' simulator points toaround twenty wavelengths required for a long distance HF skywave link[34,35]. Some experimental results for HF skywave with a spacing ofaround 0.7 wavelengths indicated high correlation [36,37]. Similarresults were obtained from our measurements in NVIS links.

c. DIDO-NVIS Experimental Results

We measured the performance of DIDO-NVIS systems with a practicaltestbed consisting of three DIDO distributed antennas 6113 fortransmission and three DIDO clients 6110 for reception. The transmittersare located in the area of Austin, Tex., as depicted in FIG. 72: TX1 incentral Austin, TX2 in Pflugerville, TX3 in Lake Austin. All threereceivers are installed with antenna spacing of about 10 wavelengths asin FIG. 73. All six transmit and receive antennas have the sameorientation with respect to the direction of the North, since our goalwas to evaluate DIDO-NVIS performance when only space diversity isavailable, without polarization diversity.

The three transmitting distributed antennas are locked to the same GPSDOthat provide time and frequency reference. The three receiving DIDOclients have free-running clocks and synchronization algorithms areimplemented to compensate for time/frequency offsets. The carrierfrequency is 3.9 MHz, bandwidth is 3.125 KHz and we use OFDM modulationwith 4-QAM.

Typical 4-QAM constellations demodulated at the three DIDO clientlocations are depicted in FIG. 74. Our DIDO-NVIS 3×3 testbed createsthree simultaneous spatial channels over NVIS links by pre-cancellinginter-user interference at the transmit side and enabling successfuldemodulation at the users' side.

We compute the symbol error rate (SER) performance as a function of theper-user SNR (PU-SNR) over about 1000 channel realizations as in FIG.75. The dots are individual measurements for all three DIDO clients 6112and the solid lines are the average per-user SER (PU-SER). The averageSER across all three DIDO clients 6112 is denoted as A-SER. About 40 dBreceive SNR is require to successfully demodulate 4-QAM constellationsin DIDO-NIVS 3×3 links with A-SER<1%. In fact, the transmit/receiveantenna configuration in our experiments yields very low spatialdiversity (due to relatively close proximity of the receive antennas,given the wavelength, and transmitters being all located on one side ofthe recevers rather than around the users). In more favorable conditions(i.e., transmitters placed around the users in circle and at largerdistance as in FIG. 61) much lower SNR (˜20 dB) is required todemodulate QAM constellations with DIDO-NVIS, as derived via simulationsin realistic NVIS propagation channels.

d. Practical Implementation of DIDO Systems in NVIS Links

Similarly to the case study 1, we provide one embodiment of DIDO-NVISsystem design as follows:

Bandwidth: 1-3 MHz, depending on HF spectrum availability. Largerbandwidths are less practical, since they require more challengingbroadband antenna designs. For example, 3 MHz bandwidth at 4 MHz carrierfrequency corresponds to fractional antenna bandwidth of 75%.

Carrier Frequency: The HF frequencies corresponding to the plasmacritical frequency of the ionosphere are between 1 and 10 MHz. Radiowaves at lower frequencies (˜1 MHz) are typically reflected by theionosphere at nighttime, whereas higher frequencies (˜10 MHz) atdaytime. The frequency of optimal transmission (FOT) at given time ofthe day varies with the SSN. In practical DIDO-NVIS systems, the carrierfrequency can be adjusted throughout the day depending on the FOTprovided by the ionospheric maps.

Transmit Power: Based on the path loss results in FIG. 71, the averagetransmit power requirement for 1 MHz bandwidth with receivers in remoteareas (i.e., man-made noise level of −133 dBm/Hz) is between 10 dBm and30 dBm, depending on QAM modulation and forward error correction (FEC)coding schemes. In industrial areas (i.e., man-made noise level of −110dBm/Hz) those levels increase of about 23 dB up to 33-53 dBm, dependingon QAM modulation and FEC coding schemes.

Modulation: We assume OFDM modulation as in FIG. 11. The cyclic prefixis 2 msec (based upon typical delay spread expected in NVIS links)corresponding to 2000 channel taps at 1 MHz bandwidth. The OFDM waveformcan be designed with 2¹⁴ tones, corresponding to ˜10% loss in spectralefficiency due to cyclic prefix. The total OFDM symbol duration(including cyclic prefix and data) at 1 MHz bandwidth is 18.4 msec.

Packet Size: is limited by the minimum channel coherence time expectedin NVIS links. The minimum coherence time is approximately 1 sec and thechannel gain can be considered constant over one tenth of that duration(˜100 msec) in the worst case scenario. Then, the packet size is aboutfive OFDM symbols. The packet size can be dynamically adjusted as thecoherence time varies over time.

CSI Estimation and Precodinq: With the system parameters above, trainingfor CSI estimation is sent every ˜100 msec (or higher, when thecoherence time increases). The users estimate/feedback the CSI and ˜5msec later (i.e., latency over the BSN feedback loop) they receive 100msec of precoded data to demodulate.

DIDO Distributed Antenna Placement Within the Coverage Area: Onepractical solution to implement DIDO-NVIS systems is to place multipleDIDO distributed antennas along the circumference of a circular regionof radius ˜100 miles as in FIG. 61. These stations are connected to eachother via a BSN that carries control information. At the speed of lightthrough optical fiber, the propagation latency along the circumferenceof radius 100 miles is ˜3.4 msec. This delay is much smaller thantypical channel coherence time in NVIS channels and can be toleratedwithout any significant performance degradation for the DIDO precoder.Note that if the optical fiber is shared across different operators,that delay may be larger (i.e., 10-30 msec) due to the packet switchednature of the Internet. Multiple DIDO-NVIS cells as in FIG. 76 can bedistributed to provide full coverage over the USA. For example, FIG. 76shows that 109 DIDO cells of radius 125 miles are required to cover theentire territory of the 48 contiguous states in the USA.

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Embodiments of the invention may include various steps as set forthabove. The steps may be embodied in machine-executable instructionswhich cause a general-purpose or special-purpose processor to performcertain steps. For example, the various components within the BaseStations/APs and Client Devices described above may be implemented assoftware executed on a general purpose or special purpose processor. Toavoid obscuring the pertinent aspects of the invention, various wellknown personal computer components such as computer memory, hard drive,input devices, etc., have been left out of the figures.

Alternatively, in one embodiment, the various functional modulesillustrated herein and the associated steps may be performed by specifichardware components that contain hardwired logic for performing thesteps, such as an application-specific integrated circuit (“ASIC”) or byany combination of programmed computer components and custom hardwarecomponents.

In one embodiment, certain modules such as the Coding, Modulation andSignal Processing Logic 903 described above may be implemented on aprogrammable digital signal processor (“DSP”) (or group of DSPs) such asa DSP using a Texas Instruments' TMS320x architecture (e.g., aTMS320C6000, TMS320C5000, . . . etc). The DSP in this embodiment may beembedded within an add-on card to a personal computer such as, forexample, a PCI card. Of course, a variety of different DSP architecturesmay be used while still complying with the underlying principles of theinvention.

Elements of the present invention may also be provided as amachine-readable medium for storing the machine-executable instructions.The machine-readable medium may include, but is not limited to, flashmemory, optical disks, CD-ROMs, DVD ROMs, RAMs, EPROMs, EEPROMs,magnetic or optical cards, propagation media or other type ofmachine-readable media suitable for storing electronic instructions. Forexample, the present invention may be downloaded as a computer programwhich may be transferred from a remote computer (e.g., a server) to arequesting computer (e.g., a client) by way of data signals embodied ina carrier wave or other propagation medium via a communication link(e.g., a modem or network connection).

Throughout the foregoing description, for the purposes of explanation,numerous specific details were set forth in order to provide a thoroughunderstanding of the present system and method. It will be apparent,however, to one skilled in the art that the system and method may bepracticed without some of these specific details. Accordingly, the scopeand spirit of the present invention should be judged in terms of theclaims which follow.

Moreover, throughout the foregoing description, numerous publicationswere cited to provide a more thorough understanding of the presentinvention. All of these cited references are incorporated into thepresent application by reference.

The invention claimed is:
 1. A multiuser (MU) multiple antenna system(MU-MAS) comprising: one or more centralized units communicativelycoupled to a plurality of distributed wireless transceivers via anetwork; the network carrying baseband or radio frequency (RF) signals;and one or a plurality of precoding units that generate a plurality ofprecoded waveforms; wherein the distributed wireless transceiverscomprise baseband or RF units to transform the precoded waveforms into aplurality of RF signals and cooperate to coordinate the transmission ofthe RF signals to jointly create a plurality of concurrent locations inspace with zero RF energy; the system further comprising: a main MU-MAScluster having at least one base transceiver station (BTS) connected toa first plurality of antennas for communicating with a target user,wherein the BTSs in the main MU-MAS cluster implement MU-MAS precodingto transmit simultaneous non-interfering data streams within the samefrequency band to a first plurality of MU-MAS users including the targetuser; and one or more interfering MU-MAS clusters, each interferingMU-MAS cluster having at least one BTS connected to a second pluralityof antennas communicating with a second plurality of MU-MAS users,wherein the BTSs in the interfering MU-MAS cluster implement MU-MASprecoding to transmit simultaneous non-interfering data streams withinthe same frequency band to the second plurality of MU-MAS users; whereinwhen the target user is located within a zone in which the target useris transmitting and receiving data streams to and from the antennas inthe main MU-MAS cluster, respectively, and is also detecting RF signalstransmitted from the antennas in the interfering MU-MAS cluster, theBTSs in the interfering MU-MAS cluster implement MU-MAS precoding withinter-MU-MAS-cluster interference (IMCI) cancellation to avoid RFinterference at the target user.
 2. The system as in claim 1 whereinimplementing MU-MAS precoding with IMCI cancellation comprises creatingzero RF energy towards the direction of the target user.
 3. The systemas in claim 1 wherein the BTSs in the main MU-MAS cluster compute MU-MASprecoding weights to pre-cancel inter-user interference with the firstplurality of MU-MAS users in the main MU-MAS cluster.
 4. The system asin claim 3 wherein the BTSs in the main MU-MAS cluster implement blockdiagonalization (BD) precoding to pre-cancel the inter-user interferencewith the first plurality of MU-MAS users.
 5. The system as in claim 1wherein the BTSs in the interfering MU-MAS cluster compute MU-MASprecoding weights to pre-cancel inter-user interference with secondplurality of MU-MAS users serviced by the interfering MU-MAS cluster andcompute additional MU-MAS precoding weights for IMCI cancellation toavoid RF interference at the target user.
 6. The system as in claim 5wherein the BTSs in the interfering MU-MAS cluster implement blockdiagonalization (BD) precoding to both pre-cancel the inter-userinterference with the second plurality of MU-MAS users and to performIMCI cancellation to avoid RF interference at the target user.
 7. Thesystem as in claim 1 wherein the BTSs in the interfering MU-MAS clusterimplement MU-MAS precoding with IMCI cancellation to avoid RFinterference at the target user only if the signal-to-interference ratio(SIR) computed from signal strength detected by the target user from themain MU-MAS cluster and interference signal strength detected by thetarget user from the interfering MU-MAS cluster is below a specifiedthreshold.
 8. A machine-implemented method within a multiuser (MU)multiple antenna system (MU-MAS) comprising: communicatively couplingone or more centralized units to a plurality of distributed wirelesstransceivers via a network, the network carrying baseband or radiofrequency (RF) signals; generating a plurality of precoded waveformsfrom one or a plurality of precoding uinits; the distributed wirelesstransceivers comprising baseband or radio frequency (RF) units totransform the precoded waveforms into a plurality of radio frequency(RF) signals and cooperating to coordinate the transmission of the RFsignals to jointly create a plurality of concurrent locations in spacewith zero RF energy; the method further comprising: associating a targetuser with a main MU-MAS cluster having at least one base transceiverstation (BTS) connected to a first plurality of antennas, implementingMU-MAS precoding on the BTSs in the main MU-MAS cluster to transmitsimultaneous non-interfering data streams within the same frequency bandto a first plurality of MU-MAS users including the target user;associating a second plurality of MU-MAS users with an interferingMU-MAS cluster having at least one BTS connected to a second pluralityof antennas; implementing MU-MAS precoding on the BTSs in theinterfering MU-MAS cluster to transmit simultaneous non-interfering datastreams within the same frequency band to a second plurality of MU-MASusers; and implementing MU-MAS precoding with inter-MU-MAS-clusterinterference (IMCI) cancellation at the BTSs in the interfering MU-MAScluster to avoid RF interference at the target user if the target useris located within a zone in which the target user is transmitting andreceiving data streams to and from the antennas in the main MU-MAScluster, respectively, and is also detecting RF signals transmitted fromthe antennas in the interfering MU-MAS cluster.
 9. The method as inclaim 8 wherein implementing MU-MAS precoding with IMCI cancellationcomprises creating zero RF energy towards the direction of the targetuser.
 10. The method as in claim 8 wherein the BTSs in the main MU-MAScluster compute MU-MAS precoding weights to pre-cancelinter-interference with the first plurality of MU-MAS users in the mainMU-MAS cluster.
 11. The method as in claim 10 wherein the BTSs in themain MU-MAS cluster implement block diagonalization (BD) precoding topre-cancel the inter-user interference with the first plurality ofMU-MAS users.
 12. The method as in claim 8 wherein the BTSs in theinterfering MU-MAS cluster compute MU-MAS precoding weights topre-cancel inter-user interference with second plurality of MU-MAS usersserviced by the interfering MU-MAS cluster and compute additional MU-MASprecoding weights for IMCI cancellation to avoid RF interference at thetarget user.
 13. The method as in claim 12 wherein the BTSs in theinterfering MU-MAS cluster implement block diagonalization (BD)precoding to both pre-cancel the inter-user interference with the secondplurality of MU-MAS users and to perform IMCI cancellation to avoid RFinterference at the target user.
 14. The method as in claim 8 whereinthe BTSs in the interfering MU-MAS cluster implement MU-MAS precodingwith IMCI cancellation to avoid RF interference at the target user onlyif the signal-to-interference ratio (SIR) computed from signal strengthdetected by the target user from the main MU-MAS cluster andinterference signal strength detected by the target user from theinterfering MU-MAS cluster is below a specified threshold.